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Clogging mechanisms in Managed Aquifer Recharge: a case study at Mining Area C
Lily Smith (21224043)
Coordinating Supervisor:
Associate Professor Ryan Vogwill, School of Earth and Environment, UWA
Co-supervisor:
Jed Youngs, Manager Hydrology, BHP Billiton West Australian Iron Ore
Clogging mechanisms in Managed Aquifer Recharge: a case study at Mining Area C SCIE9722 FNAS Master of Science Thesis
Lily Smith 208262
This thesis is submitted to fulfill the requirements for Master of Science (School of Earth and
Environment) by way of Thesis and Coursework
Faculty of Science
The University of Western Australia
May 2014
ii
Table of Contents
List of Figures .......................................................................................................................... vi
List of Tables ........................................................................................................................... ix
Abbreviations ........................................................................................................................... x
Abstract ................................................................................................................................... 11
1 Introduction .................................................................................................................... 12
1.1 Overview .................................................................................................................. 12
1.2 Aims ......................................................................................................................... 13
1.3 System Characterisation ........................................................................................... 13
1.3.1 Site Description .................................................................................................... 13
1.3.2 MAR System Characterisation ............................................................................. 14
1.3.3 Climate Characterisation ..................................................................................... 16
1.3.4 Hydrogeological Characterisation ...................................................................... 17
2 Literature Review .......................................................................................................... 21
2.1 Managed Aquifer Recharge ..................................................................................... 21
2.1.1 Definition ............................................................................................................. 21
2.1.2 Purpose ................................................................................................................ 21
2.1.3 Types .................................................................................................................... 22
2.1.4 Benefits ................................................................................................................. 22
2.2 Clogging ................................................................................................................... 22
2.2.1 Introduction .......................................................................................................... 23
2.2.2 Types and Causes ................................................................................................. 23
2.2.3 Management Options ........................................................................................... 25
2.2.4 Diagnostic Tools .................................................................................................. 25
2.3 Application of MAR in the Mining Industry ........................................................... 26
2.3.1 Mining Water Management ................................................................................. 26
2.3.2 MAR Schemes in the Pilbara Region ................................................................... 28
2.3.3 Mining Specific Operational Considerations ...................................................... 28
3 Materials and methods .................................................................................................. 29
3.1 Aquifer Response ..................................................................................................... 29
3.1.1 Hydrographs ........................................................................................................ 29
iii
3.1.2 Mounding .............................................................................................................. 29
3.2 Operational Performance .......................................................................................... 29
3.2.1 Aquifer vs In-Well Groundwater Level ................................................................. 29
3.2.2 Specific Injectivity................................................................................................. 30
3.2.3 Well Efficiency ...................................................................................................... 32
3.3 Clogging Diagnostics ............................................................................................... 34
3.3.1 Graphical Tool ..................................................................................................... 34
3.3.2 Water Quality Analysis ......................................................................................... 34
3.3.3 Bore Casing Image Analysis ................................................................................ 34
3.3.4 Saturation Index ................................................................................................... 35
4 Results .............................................................................................................................. 37
4.1 Aquifer Response ..................................................................................................... 37
4.1.1 Hydrographs ......................................................................................................... 37
4.1.2 Mounding .............................................................................................................. 38
4.2 Operational Performance .......................................................................................... 40
4.2.1 Aquifer vs In-Well Groundwater Level ................................................................. 40
4.2.2 Specific Injectivity................................................................................................. 41
4.2.3 Well Efficiency ...................................................................................................... 44
4.3 Clogging Diagnostics ............................................................................................... 46
4.3.1 Graphical Tool ..................................................................................................... 46
4.3.2 Water Quality Analysis ......................................................................................... 46
4.3.3 Bore Casing Image Analysis ................................................................................ 47
4.3.4 Saturation Index ................................................................................................... 50
5 Discussion ........................................................................................................................ 51
5.1 Aquifer Response ..................................................................................................... 51
5.2 Operational Performance .......................................................................................... 52
5.3 Clogging Diagnostics ............................................................................................... 55
6 Conclusions ..................................................................................................................... 56
7 References........................................................................................................................ 58
Appendix A – Introduction .................................................................................................. A.1
Appendix B - Literature Review ......................................................................................... B.1
Appendix C – Materials and Methods ................................................................................ C.1
Appendix D - Results ............................................................................................................ D.1
iv
Appendix E – Research Proposal ........................................................................................ E.1
v
Acknowledgements
I would like to thank my supervisors Dr Ryan Vogwill (UWA) and Jed Youngs (BHPB) for
their time and effort in guiding me through this thesis; their ongoing support and advice were
very much appreciated and the project would not have come to fruition without it.
Many thanks to Pierre Rousseau from WAFE Pty Ltd for his technical guidance and support
with the hydrogeochemistry modeling software PHREEQC.
The support of BHP Billiton West Australian Iron Ore for making the project data available
for this research is much appreciated.
vi
List of Figures
Figure 1 Mining Area C location and site layout, showing the components of the MAR
system....................................................................................................................................... 14
Figure 2 MAR schematic diagram showing the seven system components of an MAR project.
.................................................................................................................................................. 16
Figure 3 Historic monthly rainfall record at Flat Rocks gauge, 20km north of MAC............. 17
Figure 4 Geological cross section for HGA0001P looking east along 709317E (Central
Pilbara Grid), unit codes from Table 2 .................................................................................... 18
Figure 5 Geological cross section for HGA0002P looking east along 709562E (Central
Pilbara Grid) , unit codes from Table 2 ................................................................................... 18
Figure 6 Geological cross section for HGA0002P looking east along 709770 (Central Pilbara
Grid), , unit codes from Table 2 ............................................................................................... 19
Figure 7 A graphical diagnostic tool for determining different mechanisms of clogging ....... 24
Figure 8 Hydrograph at observation bore GWB0012M showing a declining regional
groundwater trend prior to injection (1997 – 2012) ................................................................. 32
Figure 9 An example of a 1m section showing both the raw and enhanced OTV image ........ 35
Figure 10 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0001P 37
Figure 11 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0002P 38
Figure 12 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0003P 38
Figure 13 Pre-injection groundwater surface in mRL from the 09/02/2012 (0.1m contour
intervals)................................................................................................................................... 39
Figure 14 Post-injection groundwater surface in mRL from the 11/4/2013 (0.1m contour
intervals)................................................................................................................................... 39
Figure 15 Groundwater mounding in m following 12-months of injection (0.1m contour
intervals)................................................................................................................................... 40
vii
Figure 16 Plot of the difference between the injection bore and the closest observation bores
groundwater levels .................................................................................................................... 40
Figure 17 Long-term specific Injectivity, SiL over time for each injection bore ..................... 42
Figure 18 Short-term specific injectivity, SIS over time for each injection bore ..................... 43
Figure 19 The cumulative deviation from mean rainfall plot showing a dry period prior to the
commencement of the groundwater injection trial ................................................................... 44
Figure 20 Matching theCDFM plot with the GWB0012M hydrograph to determine a
relationship ............................................................................................................................... 44
Figure 21 Well efficiency (%) showing results for both pre-injection (dark) and post-injection
(light) datasets for HGA0001P (diamonds), HGA0002) (squares) and HGA0003P (triangles).
.................................................................................................................................................. 46
Figure 22 Flow resistance in terms of in-well mounding overlain with the standard curves. .. 47
Figure 23 Field measured turbidity for HGA0001P and HGA0002P (HGA0003P not
available) showing the target of <5 NTU ................................................................................. 48
Figure 24 Bar chart showing the percentage of biofouling on the slotted PVC screens from the
image analysis for the pre-injection, post-injection and post-test pumping scenarios ............. 48
Figure 25 Percentage of biofouling with depth for HGA0001P, HGA0002P and HGA0003P
showing the slotted PVC and stainless steel wire wound screen sections................................ 49
Figure 26 Saturation Index (SI) versus time for carbonate minerals (calcite and dolomite) .... 50
Figure 27 Saturation Index (SI) versus time for sulphate minerals (bartite, gypsum and
anhydrite) .................................................................................................................................. 51
Figure A-1 Location of Flat Rocks rainfall gauge (20km north of Mining Area C), DoW site
505011 .................................................................................................................................... A.2
Figure A-2 Stratigraphic Units of the Hamersley Region. ................................................ A.3
Figure A-3 Construction bore log for HGA0001 (TP4) ......................................................... A.4
Figure A-4 Construction bore log for HGA0002 (TP5) .................................................... A.5
viii
Figure A-5 Construction bore log for HGA0003P (TP3) ...................................................... A.6
Figure A-6 Local 20k geology interpretation at the injection site, A Deposit, Mining Area C..
................................................................................................................................................ A.7
Figure A-7 MAR conceptual model....................................................................................... A.9
Figure B-1 Types of MAR schemes .................................................................................. B.3
Figure C-1 Casing Image Analysis: an example of the Image Histogram, which counts the
number of pixels of given colour (Index: Black = 0), to determine the percentage of
biofouling. ............................................................................................................................... C.2
Figure C-2 Saturation Index modeling: field-determined pH versus laboratory determined pH
................................................................................................................................................. C.3
Figure C-3 Saturation Index modeling: field determined pH versus time ............................. C.3
Figure D-1 The Hantush-Bierschenk method for determining ∆S ........................................ D.2
Figure D-2 The Hantush-Bierschenk method for determining parameters B and C ............. D.6
Figure D-3 The reduction in well efficiency (EW) between pre-injection and post-injection
data ......................................................................................................................................... D.9
Figure D-4 Results of the sensitivity analysis where the well efficiency at each step is
calculated using a range of P values .................................................................................... D.10
Figure D-5 Sensitivity Analysis for Saturation Index (SI) with respect to temperature for
carbonates and sulphates ...................................................................................................... D.17
ix
List of Tables
Table 1 MAR bore details (Refer to Table 2 for codes of screened units) ............................... 15
Table 2 Geological reference table for unit codes .................................................................... 20
Table 4 Static Water level for the injection bores recorded on 9/2/12 ..................................... 31
Table 5 Details of the step-drawdown tests .............................................................................. 32
Table 6 Static Water level for the injection bores recorded on 9/2/12 ..................................... 34
Table 7 Dissolution reactions and ion activity products (IAP) for minerals included in the
analysis ..................................................................................................................................... 36
Table 8 Jacobs equation coefficients determined using the Hantush-Bierschenk method ....... 45
Table 9 Assumptions of the Huntush-Bierschenk method ....................................................... 54
Table A-1 Name and description of geology codes used in. ................................................. A.8
Table B-1 Examples of MAR systems ................................................................................ B.2
Table C-1 Casing Image Analysis: The colour settings used in the downhole camera study for
each of the images. Note the HGA0003P pre-injection image quality was too poor to analyse.
................................................................................................................................................ C.2
Table D-1 The Hantush-Bierschenk method for determining Sw(n)/Qn .................................. D.5
Table D-2 Water quality sample results undertaken during injection with the ANZEC 2000
Guideline values; grey indicates that the analyte is below the detectible limit .................... D.12
Table D-3 Image quality estimates and comments on image reliability for the downhole
camera study for each 1m section of HGA0001P, HGA0002P and HGA0003P. ............... D.13
Table D-4 Key inputs and outputs for the PHREEQC modeling (red = average values used in
the absence of measured values) .......................................................................................... D.16
x
Abbreviations
AOC Assimilable Organic Carbon
ASR Aquifer Storage and Recovery
ASTR Aquifer Storage, Transport and Recovery
BIF Banded Iron Formation
BOM Bureau of Meterology
BWT Below Water Table
DEC Department of Conservation
DOC Dissolved Organic Carbon
DoW Department of Water
FMG Fortescue Metals Group
GDE Groundwater Dependent Ecosystems
IGRAC International Groundwater Resource Assessment Centre
MAC Mining Area C
MAR Managed Aquifer Recharge
mbgl meters below ground level
MFI Membrane Filtration Index
NTU Nephelometric Turbidity Units
NWQMS National Water Quality Management Strategy
ORP Oxidation-Reduction Potential
OSA Overburden Storage Area
PFI Parallel Filter Index
PHREEQC PH (pH), RE (redox), EQ (equilibrium), C (programming language)
PVC Polyvinyl chloride
SWL Static Water Level
TD Tertiary Detritals
TOC Total Organic Carbon
TSS Total Suspended Solids
VWP Vibrating Wire Piezometer
11
Abstract
Managed Aquifer Recharge or MAR is a well-established method of sustainable water
management. The changing climate, growing population, effects of urbanisation and surface
water scarcity increase pressure on future global water resources. While MAR has been
primarily utilised for potable and agricultural purposes in the past, it has future potential to
form an integral part of water management in the mining industry. It is an effective and
sustainable method for disposing of surplus water in an operational mining capacity and can
reduce the long term dewatering footprint of below water table deposits. Clogging of the
injection well and surrounding aquifer matrix is a common operational issue that results in
reduced permeability of injection surfaces. Clogging can occur via physical, chemical or
biological mechanisms or a combination thereof. The MAR scheme at BHP Billiton‟s Mining
Area C in the central Pilbara was used as a case study to investigate the potential for clogging
mechanisms to affect operational performance.
The MAR system at MAC injects surplus dewatering supply from mineralised iron-orebody
aquifers into the regional karstic dolomite aquifer. The trial commenced in April 2012 and
available data was analysed until April 2013. The regional aquifer showed a maximum 5m
increase in groundwater levels in response to the trial. The operational performance of the
injection bores decreased throughout the study period and one injection bore was shut down
prematurely due to poor performance. The well efficiency significantly decreased in two of
the three bores as a result of injection. A comparison of bore casing images pre- and post-
injection showed a significant increase in the clogging layer area. Test pumping had a minor
remediation effect, reducing the clogging layer by 10-15%. As such, clogging mechanisms
had a moderate impact of the operation of the MAR system at MAC.
Key words: Managed Aquifer Recharge, clogging, mining, Pilbara
12
1 Introduction
1.1 Overview
Managed Aquifer Recharge (MAR) is the process of enhancing natural rates of recharge to
groundwater systems and is becoming an increasingly important water management tool
globally. MAR schemes can take on a number of different forms, with the Aquifer Storage
and Recovery (ASR) and infiltration basin type being the most common. MAR schemes have
been successfully implemented in Australia and around the world for the purposes of
stormwater harvesting and wastewater recycling to secure additional water supplies and
maintain environmental outcomes. However, there is huge potential for MAR to be applied to
the mining industry to aid mine site water management.
Clogging at the injection surface poses one of the most challenging and persistent technical
issues for the operation of an MAR scheme (Dillon et al. 2001). Clogging can occur via
physical, chemical and biological processes and several diagnostic methods exist to predict
the clogging potential. An understanding of the clogging formation process and potential in
each MAR system enables efficient operation and maximises the longevity of MAR
infrastructure.
Using a case study at Mining Area C (MAC), 120km northeast of Newman, this thesis will
explore the role of MAR in mining and asses the injection performance of the system with
regards to potential clogging mechanisms. In November 2010, MAC commenced Below
Water Table (BWT) mining and associated dewatering activities. Groundwater abstracted
from in-pit and ex-pit borefields is used for dust suppression and ore handling and processing.
The mine has reached a positive water balance scenario, where dewatering supply is greater
than use. Management option to handle the surplus water generated by BWT mining
operations in the past have included storage in in-pit lakes, evaporation fans and infiltration
and evaporation with sediment ponds. MAR was identified as the preferred option and
manages the surplus water scenario in a sustainable and environmentally sensible manner.
MAR has the potential to minimise the mine site drawdown footprint by conserving the
groundwater resource for future direct use or targeted mitigation. In addition, it is in-line with
the DoW heirachy of responsible water use as outlined in the Pilbara water in mining
Guidelines (DoW(a), 2009).
13
The MAC MAR system was commissioned in April 2012 and continuous injection began in
August 2012. This thesis will review field data collected between April 2012 and April 2013.
Prior work identified clogging as a potential operational risk to the system; through the
precipitation of carbonates, the build-up of sediments or the growth of iron bacteria (BHP
Billiton, 2011).
1.2 Aims
This thesis seeks to achieve the following aims to investigate and understand the operational
risks in relation to the MAR system at MAC:
Describe the hydrological response to the injection trial in terms of aquifer
mounding, including both the spatial distribution of mounding observed in the
monitoring bores and the in-well response of the injection bores;
Analyse and quantify the operational performance of the injection system
throughout the duration of the trial;
Apply diagnostic tools to determine the clogging mechanism, if applicable, in the
event of decreasing operational performance.
1.3 System Characterisation
1.3.1 Site Description
Mining Area C (MAC) is a BHP Billiton owned and operated open-pit iron ore mine located
120km northeast of Newman in the Pilbara region of Western Australia. Mining activities are
conducted within tenement ML281SA. Figure 1 shows the site layout including key
components of the MAR scheme, final pit outlines of each deposit and the Overburden
Storage Areas (OSA).
14
Figure 1 Mining Area C location and site layout, showing the components of the MAR system (Figure created by
author)
1.3.2 MAR System Characterisation
Figure 2 shows the MAR design schematic with the seven components common to all MAR
projects as outlined by Dillon et al. (2009). Injection water is sourced from dewatering of the
mineralised Marra Mamba orebody aquifers at C and E Deposit (number of bores may vary
according to operational requirements). Water is transferred through a PVC pipe network to a
settling pond at A deposit, where a skid-mounted centrifugal pump drives the surplus water to
the injection bores. The infrastructure is designed to a 24ML/day injection capacity with an
expected annual discharge of 5.84 GL (BHP Billiton, 2011). Water is discharged into the
Paraburdoo Dolomite Member of the Wittenoom Formation via three 8” PVC injection bores
situated in A Deposit; HGA001P, HGA0002P and HGA0003P. The injection bores were
originally constructed and used as water supply bores and have been retrofitted with an orifice
plate connected to 75mm diameter layflat hose to facilitate injection. Table 1 shows that
HGA0001P is screened in the Wittenoom dolomite only, with HGA0002P and HGA0003P
screened in both the dolomite and the tertiary detritals and summarises information on the
adjacent observation bores. Construction logs are provided in Appendix A; Figure A-3,
Figure A-4 and Figure A-5 for further reference.
15
Table 1 MAR bore details (Refer to MAC is located in the Fortescue River basin within the
Weeli Wolli Creek system. The regional groundwater system flows to the east towards Weeli
Wolli Spring, Prior to mining, groundwater levels at MAC were 662mRL and decreasing to
555mRL at Weeli Wolli Spring (Golder, 2011). The convergence of groundwater flow with
outcropping basement rock in the east causes groundwater levels to rise and form the Weeli
Wolli Spring (RPS Aquaterra, 2008).
The groundwater at MAC is of potable quality and is calcium-magnesium bicarbonate rich.
Water quality analysis conducted by Golder (2011) indicates that the groundwater is weakly
acidic to weakly alkaline, with pH ranging from pH6.3 to pH 8.2. The salinity ranges from
258 to 642 mg/L with temperatures around 30C. Barber (2010) states that the level of
dissolved oxygen in both the source groundwater and the receiving groundwater indicates that
redox reactions are unlikely to take place due to the mixing of waters. However, there is some
potential for dolomite dissolution given the low levels of carbonate minerals (dolomite and
calcite) and sulphate minerals (anhydrite, gypsum and barite) in the source water.
16
Table 2 for codes of screened units)
Bore Depth (m)
Screens Date Drilled
Final Airlift Yield (L/s)
SWL (mbTOC) Unit Interval Depth Date
HGA0001P 118 OB 82-115 30/03/1998 16 45.27 18/04/1998 HGA0002P 136 OB/ TD 77.5-125.5 05/04/1998 >20 42.63 19/04/1998 HGA0003P 115 OB/TD 48-106 30/06/1997 N/A 42.66 05/10/1997 Closest adjacent observation bore to HGA0001P (10.8m) HGA0035M 66 TD 54-65 02/04/2012 N/A 52.35 04/04/2012 Closest adjacent observation bore to HGA0002P (15.3m) HGA0036M 66 OB/TD 44-65 03/04/2012 N/A 46.55 04/04/2012 Closest adjacent observation bore to HGA0003P (12.8m) GWB0012M 91 OB 77-91 25/05/1997 4 42.49 15/06/1997
Figure 2 MAR schematic diagram showing the seven system components of an MAR project outlined by Dillon et al.
(2009).
1.3.3 Climate Characterisation
The central Pilbara climatic setting is arid-tropical with high summer rainfall influenced by
tropical maritime and continental air masses. Historic monthly rainfall data was available
from the Flat Rocks rainfall gauge (DoW site 505011) from 1972 to 2013. The MAC region
receives an average annual rainfall of 404mm and the rainfall record is dominated by cyclonic
events as shown in Figure 3. A location map for the rainfall gauge is available in Figure A-1
in Appendix A.
17
Figure 3 Historic monthly rainfall record at Flat Rocks gauge, 20km north of MAC
1.3.4 Hydrogeological Characterisation
MAC is located in the Hamersley Basin on the northern flank of a large-scale regional
anticline which strikes east to west. The stratigraphic sequence, as provided in Figure A-2
Appendix A, dips northwards. The two dominant iron bearing stratigraphic units are the
mineralised Mount Newman Member of the Marra Mamba banded iron formation (BIF) and
the Brockman BIF which outcrops in the Packsaddle Ranges.
Complex faulting and folding is a dominant feature of the local geology at MAC. A low
permeability dolerite dyke runs through E Deposit which has implications on local
groundwater flow. The local 20,000km surface geology interpretation at the injection site is
provided in Appendix A, Figure A-6 with a Table A-1 explaining the geology codes.
The Paraburdoo dolomite Member of the Wittenoom Formation constitutes the regional
aquifer at MAC, along with saturated alluvial valley fill and scree detritals with calcrete
lenses. The hydraulic conductivity varies from high in significantly weathered regions to low
where fresh dolomite is predominant. Sufficiently mineralised zones of the Mount Newman
Member of the Marra Mamba Iron Formation form localized orebody aquifers. The West
Angela semi-permeable shale separates these aquifers to varying degrees along the strike of
the deposit.
0
50
100
150
200
250
300
350
1972 1976 1980 1984 1988 1992 1997 2001 2005 2009 2013
Tot
al M
onth
ly R
ainf
all (
mm
)
Historic Rainfall Record at Flat Rocks (505011)
Cyclone Joan - Dec 1975478.4mm
18
The conceptual model of the MAR system is shown in Figure A-7, Appendix A, which
highlights the major geological formations, conceptual abstraction and dewatering bores and
indicative pre- and post- injection water levels. Geological cross sections were prepared using
the 3-dimensional modeling software Leapfrog (version 2.1.1), showing the intersection with
the regional dolomite aquifer (OB = blue) in Figure 4 to Figure 6. The cross sections show
northward dipping stratigraphy overlain by 50 to 90m of tertiary sediment deposits.
Figure 4 Geological cross section for HGA0001P looking east along 709317E (Central Pilbara Grid), unit codes from
Table 2
Figure 5 Geological cross section for HGA0002P looking east along 709562E (Central Pilbara Grid) , unit codes from
Table 2
19
Figure 6 Geological cross section for HGA0002P looking east along 709770 (Central Pilbara Grid),, unit codes from
Table 2
MAC is located in the Fortescue River basin within the Weeli Wolli Creek system. The
regional groundwater system flows to the east towards Weeli Wolli Spring, Prior to mining,
groundwater levels at MAC were 662mRL and decreasing to 555mRL at Weeli Wolli Spring
(Golder, 2011). The convergence of groundwater flow with outcropping basement rock in the
east causes groundwater levels to rise and form the Weeli Wolli Spring (RPS Aquaterra,
2008).
The groundwater at MAC is of potable quality and is calcium-magnesium bicarbonate rich.
Water quality analysis conducted by Golder (2011) indicates that the groundwater is weakly
acidic to weakly alkaline, with pH ranging from pH6.3 to pH 8.2. The salinity ranges from
258 to 642 mg/L with temperatures around 30C. Barber (2010) states that the level of
dissolved oxygen in both the source groundwater and the receiving groundwater indicates that
redox reactions are unlikely to take place due to the mixing of waters. However, there is some
potential for dolomite dissolution given the low levels of carbonate minerals (dolomite and
calcite) and sulphate minerals (anhydrite, gypsum and barite) in the source water.
20
Table 2 Geological reference table for unit codes
Group Formation Member Code Description
Tertiary-age sediments
SZ Surface scree
TD3 Red tertiary detritals; transported material; pisolitic well-rounded gravels and magehite; typically low permeability
TD2 Yellow-brown Tertiary detritals; contains clay and calcrete lenses; typically low permeability
TD1
Red Ochre Detritals; fine-grained sediment deposits with large hematite clasts; formed from alluvial material from the Wittenoom and Marra Mamba Iron Formations; generally Fe>60%; typically low permeability
Ham
ersl
ey G
roup
Witt
enoo
m F
orm
atio
n
Paraburdoo OB Blue-grey dolomite; ranges from fresh to highly weathered; represents the regional aquifer in high permeability zones with dominant fracturing and weathering
West Angela WA2
Green-grey shale; low permeability with some fracturing allowing connection to the Marra Mamba formation
WA1 Cherty BIF followed by shale; can be enriched with Fe>50%
Mar
ra M
amba
Iron
Form
atio
n
Mount
Newman
N3 BIF with minor shale interbeds; geothetic mineralisation with carbonaceous shales; where mineralised (Fe>60% typically) this unit forms a localised aquifer associated with the ore deposit and enrichment favours the N3 unit
N2
N1
MacLeod MM Shaley, cherty BIF with several chert pod; horizons; potential enriched with Fe>30%; moderate permeability where mineralised
21
2 Literature Review
This literature review investigates the fundamentals of a MAR system, common operational
issues and the practicalities of MAR in the mining industry.
2.1 Managed Aquifer Recharge
2.1.1 Definition
Managed aquifer recharge is the “intentional recharge of water to aquifers for subsequent
recovery or environmental benefit” (NWQMS 2009 pg 13). The process has also been
referred to as enhanced recharge, water banking and sustainable underground storage in the
literature (Dillon 2005). The definition specifies „intentional‟ recharge to separate MAR from
incidental or unintended recharge processes such as the effects of land clearing, over
irrigation and increased runoff from urbanisation. Recently, the term “artificial recharge” has
been effectively replaced with MAR to avoid the negative connotations associated with the
perceived unnatural and non-sustainable process.
2.1.2 Purpose
MAR systems may be implemented for a number of purposes:
to harvest urban stormwater to supplement water resources (Page et al. 2011;
Vanderzalm et al. 2010; Dillon et al. 1999);
to reclaim wastewater to supplement water resources (Page et al. 2010; Bosher et al.
1998; Asano and Levine 1998);
to sustain environmental flows and phreatophytic vegetation (Naumburg et al. 2005);
and to act as a barrier to prevent saline intrusion (Daher et al. 2011; Shammas 2008);
to manage water generated from dewatering activities in open pit mining operations
(Youngs et al., 2010; Clarke, 1983).
The use of MAR to capture and store stormwater to reduce demand on conventional water
resources has become increasingly popular in Australia over the last decade. The largest
Australian MAR project is located in Queensland‟s Burdekin Delta, where recharge of
100GL/year via infiltrations ponds maintains sugar cane production. MAR initiatives have
been implemented with success worldwide; with India, USA, Sweden, Finland, New Zealand,
22
France and Germany leading the way with application of groundwater recharge management
schemes (IGRAC 2012).
2.1.3 Types
MAR encompasses a wide variety of water management systems, which vary with recharge
method, source of recharge water, end use of recovered water, scale and complexity. Aquifers
may be recharged by two methods: (a) the injection of source water directly into the target
aquifer through screened wells, or (b) the infiltration of source water through open basins,
galleries or channels. Recharge water may be sourced from drinking water treatment plants,
sewage treatment plants, harvested storm water, irrigation districts, ephemeral streams,
industrial specific sources (Bouwer 2002) or, as at MAC, excavation dewatering/
Table B-1 in Appendix B lists an example of each type of MAR system, with an associated
schematic from Dillon (2005) in Figure B-1. The ASR type of MAR scheme shall be the
focus of this literature review, as it is the model for the case study at Mining Area C.
2.1.4 Benefits
ASR has many benefits over surface water storages structures such as dams and reservoirs
making it an efficient option for long-term water storage. These include (NWQMS 2009;
Pyne 2006; Bouwer 2002; Kimrey 1989):
Low capital installation costs;
Low evapotraspiration loss from the aquifer;
Multi-purpose capacity for water quality treatment in addition to storage;
Reduced project area footprint;
Low potential for structural failure (i.e. dam wall failure);
Reduced potential for mosquito habitat;
Flexible system size to meet incremental growth in water demand;
Reduced potential for pollution or damage by sabotage or other hostile action;
Improved reliability of existing supplies
While the benefits of ASR have been widely publicised, the ongoing energy requirements and
operational cost of maintaining an ASR scheme is seldom touched on in the literature.
2.2 Clogging
23
2.2.1 Introduction
A common operational issue affecting ASR schemes is clogging of the recharge surface,
gravel pack or surrounding aquifer matrix that, in serious cases, can lead to the abandonment
of projects. Also known as aquifer plugging, the Australian MAR Guidelines define clogging
as “the reduction in permeability of a porous medium” (NWQMS 2009, p114). Clogging
leads to a reduction in flow rates, which limits the volume of water stored in the aquifer, or an
increase in head to maintain a constant recharge rate. It is important to understand the types
and causes of various forms of clogging and the associated management options, to define
source water treatments needs and maximize the operational life of the injection system.
2.2.2 Types and Causes
Clogging occurs due to reactions between the source water, target water and the aquifer
matrix as result of physical, chemical or biological mechanisms. Pyne (2005) identified that
following processes could be responsible for clogging:
Air entrapment;
Deposition of total suspended solids (TSS);
Biological growth;
Geochemical reactions;
Particle rearrangement in the aquifer materials.
Each process is discussed in detail below:
Air entrapment or gas binding is caused by the cascading of water inside the injection well
casing or air entering the recharge pipe network under negative pressure, producing air
bubbles that may block pore spaces in the aquifer matrix and screened casing. It is similar to
bubble lock which can occur during bore/well development. The entrained air increases the
oxidation-reduction potential (ORP), which promotes microbial activity and geochemical
reactions, leading to further clogging. Air entrapment can also occur due to the release of
dissolved gases through temperature or pressure changes or as a metabolic byproduct of
microbial activity (release of nitrogen or methane). Pyne (2005) suggests that clogging by air
entrapment is characterised by a rapid increase in flow resistance as shown in Figure 7.
24
Figure 7 A graphical diagnostic tool for determining different mechanisms of clogging (Pyne, 2005)
The accumulation of organic and inorganic suspended solids, such as clay and silt particles,
algae cells or their tests (diatoms), microorganism cells, can form a low permeability
clogging layer on injection surfaces (Bouwer 2002). Both Dillon et al. (2001) and Youngs et
al. (2010) assert that the deposition of suspended sediments is the most frequently reported
form of clogging.
Microbial clogging occurs through the growth of microorganisms and the production of
biofilms (extracellular polysaccharides). Pyne (2005) states that clogging due to biological
growth is not well understood. Recharge waters rich in organic carbon, nitrogen and
phosphorus, promote biological clogging and it is a commonly reported issue in recharge
basins (NWQMS 2009). Schuh (1990) identified that biological clogging in surface
infiltration systems can vary seasonally, in response to changes in water temperature and
viscosity.
Chemical clogging is the result of mineral precipitation affecting aquifer permeability.
Common geochemical reactions are the precipitation of calcium carbonate (calcite), gypsum,
phosphate, iron and manganese oxide hydrates (Bouwer 2002; Pyne 2005). Bacteria catalyse
many geochemical reactions therefore it can be difficult to separate chemical clogging from
biological clogging. These reactions occur due to the changes in redox conditions inherent in
injection of oxygenated water into typically reduced aquifers.
25
Particle rearrangement in the aquifer matrix, caused by repeated cycles of recharge and
recovery, can affect aquifer permeability (Olsthoorn 1982). Australian MAR Guidelines
(NWQMS 2009) fail to identify the particle rearrangement as a clogging mechanism however
Pyne (2005) states that particle rearrangement is not an important mechanism in aquifer
clogging but still must be considered.
2.2.3 Management Options
A well-designed and constructed system is critical to the effective operation of an MAR
scheme. Brown et al. (2000) suggested that air entrapment clogging issues could be
eliminated through the design phase in an ASR case study at Hamersley Iron‟s Nammuldi
iron ore mine in the Pilbara. Youngs et al. (2010) suggested that the rapidly constructed pipe
network, paired with poor scouring practices, promoted physical clogging of injection wells at
FMG‟s Cloudbreak operation.
Several options to manage injection well clogging are described in the literature:
Injection well redevelopment: The periodic redevelopment or backflushing of
injection wells by airlifting or pumping is the preferred method to manage clogging
according to Pyne (2005). The frequency of redevelopment depends on the rate of
clogging and can vary from daily to annually.
Pre-treatment of injection water: This is common for reinjection schemes where the
end use is for potable purposes or the quality of the source water is significantly lower
than the target aquifer. Bouwer (2002) indicates that in addition to reducing the effects
of clogging, pre-treatment of water enables the protection of the receiving
groundwater quality.
Alterations to MAR infrastructure: Youngs et al. (2010) remediated the physical
clogging of FMG‟s Cloudbreak operation by removing the slotted PVC casing, in
addition to well redevelopment.
Chemical treatment: Chlorine and chemical treatments such as mineral acids,
organic acids, biodispersants, surfactants and enzymes are utilised as a rehabilitation
procedure (Pyne 2005). It is effective against biological clogging but is limited in
mitigating physical clogging.
2.2.4 Diagnostic Tools
26
Various techniques have been reported on in the literature to predict clogging potential. These
include:
Membrane Filtration Index (MFI);
Water quality parameters;
Laboratory column experiments;
Numerical modeling;
Graphical techniques
The MFI method provides a relatively easy field assessment of physical clogging potential
(Dillon et al. 2001). Membrane filtration tests are used to develop an MFI. The test involves
passing recharge water through a membrane of fixed aperture at a constant pressure whilst
measuring the decline in flow rate. The MFI is then determined graphically from the slope of
the linear portion of the time/volume (t/V) vs. volume (V) plot. Dillon et al. (2001) states that
the greater the slope, the higher the MFI and the greater potential for physical clogging. As
the standardised membrane is unlikely to be representative of the pore spaces in the aquifer,
the test provides a guide only and cannot be relied upon as an absolute measure. Fracture-
dominated flow further reduces the relevance of MFI as a tool.
Water quality parameters can be useful indicators of clogging potential. Measurement of
turbidity and TSS indicate physical clogging while total organic carbon (TOC), dissolved
organic carbon (DOC) and assimilable organic carbon (AOC) indicate biological clogging
(NWQMS 2009). Laboratory column studies, also known as the parallel filter index (PFI), are
determined by passing recharge water through columns filled with aquifer material (Bouwer
2002; Wood et al. 2005; Rinck-Pfeiffer et al. 2000). Due to the small-scale nature of the PFI
test, this method is not usually representative of the field scale processes. Youngs et al. (2010)
applied the PHREEQC geochemical model to determine the potential of chemical clogging.
The model relies on water chemistry and hydrogeological data to predict the potential for
mineral precipitation. Simple graphical techniques to predict clogging have been proposed by
Pyne (2005), where the relationship between resistance to flow and time is compared with
standard curves for each clogging type (Figure 7).
2.3 Application of MAR in the Mining Industry
2.3.1 Mining Water Management
27
There is huge potential for MAR to be applied to the mining industry to aid mine site water
management, although only few studies investigate this prospect. The Australian MAR
Guidelines (NWQMS 2009) and investigations into legislation and policy governing MAR in
Australia (Ward and Dillon 2012) make no reference to the potential for MAR in a mining
context.
Dewatering of open pits and underground mining areas to enable safe conditions for below
water table (BWT) mining can result in large volumes of water abstracted from the orebody
aquifer. Current water management practices endeavor to use this supply for operational
requirements, which include dust suppression and ore processing. However, often the
dewatering abstraction volumes exceed mine site water demand. In such cases of water
surplus, the simplest approach is to discharge excess water to the surface water environment.
The water is lost as a resource for the mine, and most of it is is wasted as evapotranspiration..
Over the life of mine, water balances can fluctuate between water surplus and water deficit
depending on mine planning, pit sequencing of BWT deposits and climatic conditions. MAR
has the potential to buffer these fluctuations by banking water during periods of water surplus
to meet future water demand in a deficit scenario.
In addition to managing fluctuations in the mine site water balance, MAR has several benefits
over water management strategies currently applied in the industry. Firstly, the disposal of
excess dewatering volumes into ephemeral surface water system often leads to negative
ecological and cultural implications. Youngs et al. (2010) state that the constant discharge
provides a water source for ecosystems, which may then become dependent on mine site
operations, and acknowledged that surface discharge is discouraged by traditional landowners
in the Pilbara. Secondly, the reinjection of the dewatering surplus reduces the net groundwater
drawdown of the mine site operations. MAR can also be used to mitigate impacts to
groundwater dependant ecosystems (GDE) proximal to mine dewatering and to sustain
borefields, such as Ophthalmia Dam in the Pilbara (DoW, 2009).
Compared to the conventional MAR projects intended to secure potable supply, MAR
schemes in an operational mining environment are designed „fit for purpose‟. This means that
mining-related MAR schemes are typically designed and constructed rapidly on a larger scale,
operated over a shorter life-of-project duration with less emphasis on control of water quality.
This is particularly applicable to the Pilbara environment as the source water is typically of
28
high quality, similar to the receiving water, and the environmental water requirements are less
stringent the human health requirements.
2.3.2 MAR Schemes in the Pilbara Region
The potential for ASR as a mine site water management tool in the Pilbara mining region of
Western Australia has had limited attention in the scientific literature. Only few authors have
investigated this prospect (Windsor et al. 2011; Youngs et al. 2010; Brown et al. 2000; Clark
and Kneeshaw 1983). By examining two ASR case studies, Brown et al. (2009) contended
that in addition to meeting future water demand, MAR could reduce impact to the
surrounding surface water environment.
Reductions in impacts to GDEs are another potential benefit. The successful implementation
of a relatively large and complex MAR scheme at Fortescue Metals Group (FMG)‟s
Cloudbreak operation proves that MAR is a viable option for water managers in the mining
industry and is an „ideal‟ tool for the Pilbara region (Youngs et al. 2010). The scheme consists
of a saline and fresh water injection system and was implemented to bank fresh water for
future use and to maintain ecological water requirements of the nearby Fortescue Marsh
(Youngs et al. 2010).
2.3.3 Mining Specific Operational Considerations
Brown et al. (2000) identified three issues specific to the application of MAR in a mining
context:
1. The potential for injected water to re-circulate back into the dewatered orebody
aquifer, with implications for the efficiency of dewatering operations. The optimal
distance between dewatering operations and injection wells is dictated by the degree
of hydraulic connectivity between the orebody aquifer and the target injection aquifer.
Numerical modeling can be applied to predict the volumes of reticulated water
(Youngs et al. 2010).
2. The practical requirement of periodic well redevelopment due to a reduction in
hydraulic performance from the effects of injection well and aquifer clogging
(discussed further in Section Clogging) and the implications for the active mining
operations.
3. The impact of injecting poor quality water from the active mining area into a high
quality natural groundwater environment.
29
It should be noted that (3.) is not an issues solely related to MAR in a mining environment.
Studies concerning the injection of treated wastewater to secure drinking water supply
(Bosher et al. 1998; Asano and Levine 1998; Pavelic and Dillon 1997) have also highlighted
the issue of managing the interaction of low quality source water with a higher quality target
aquifer. Issues not identified by Brown et al. (2002) include the complexity added mining
operation by the injection infrastructure and the effect of reinjection on mining operations
downstream of the MAR site with the implications on their water balance and dewatering
requirements.
3 Materials and methods
3.1 Aquifer Response
3.1.1 Hydrographs
The groundwater level data was plotted alongside injection volumes to review the aquifer
response to injection over time. Each plot shows the injection groundwater level within the
bore, the static water level within the bore for periods when the system was not operating and
the water level in the closest adjacent observation bore (as outlined in Table 1).
3.1.2 Mounding
Groundwater level data (in mRL) was analysed and contoured using Surfer (version 10.2.601)
to create groundwater surface maps of pre- and post-injection scenarios. The raw point data
was interpolated using the kriging method to create a data grid file (.grd). The aquifer
response to the 12 month injection period is summarised by subtracting the post-injection grid
(11/04/2013) from the pre-injection grid (09/02/2012). The „pre-injection‟ date of 09/02/2012
was selected as it best represented the groundwater levels prior to any feasibility, testing and
commissioning works. The pre-injection grid was generated from six available monitoring
bores at the time and the post injection grid from eighteen bores, as more were constructed
and data routinely collected.
3.2 Operational Performance
3.2.1 Aquifer vs In-Well Groundwater Level
30
Groundwater level data within the injection bore was compared to the closest observation
bore. Examining the temporal trends of the difference between the water levels gives an
indication of operation performance; if the difference increases over time, the injection
performance is decreasing. The difference metric, D, is calculated according to:
Where D = difference metric (m)
I = Water level in the injection bores (mRL)
O = water level in the observation bore (mRL)
Only groundwater level data recorded during periods of injection was utilised.
3.2.2 Specific Injectivity
Specific injectivity is a metric used to define the relative strength of reinjection bores and is of
similar nature to the specific capacity term used for abstraction bores (Miller, 2001). It is
defined by the ASCE (2001) as the rate of injection divided by the total drawup within the
well and has previously been used in analysis of mine-site reinjection systems at FMG‟s
Couldbreak project by Youngs et al. (2010). A decrease in specific injectivity over time
indicates poor injection performance and the potential presence of a clogging mechanism.
Two approaches were used to analyse specific injectivity; long-term specific injectivity, SIL,
and short-term specific injectivity, SIS, according to the following equations:
Where SIL = long-term specific injectivity (m3/d/m)
VT = total volume injected since 1/8/2012 (m3)
SWT = total drawup within the well since 1/8/2012 (m)
T = total time since 1/8/2012 (d)
Where SIS = short-term specific injectivity (m3/d/m)
V = volume injected since previous measurement (m3)
31
Sw = drawup within the well since previous measurement (m)
t = time since previous measurement (d)
The analysis was restricted to a period of relatively continuous injection from 1/8/2012 to
1/04/2013. The period of continuous injection for HGA0003P ended on the 12/11/2012 due to
a management decision regarding the injection bore‟s poor performance.
Table 3 outlines the initial groundwater levels used to calculate SWT for long-term specific
injectivity prior to continuous injection.
Table 3 Static Water level for the injection bores recorded on 9/2/12
Injection Bore HGA0001P HGA0002P HGA0003P SWL 9/2/12 577.16mRL 577.13mRL 577.01mRL
Both the water level and volume data were collected at weekly intervals. However due to
operational requirements on the mine site, water level data was not collected on the same day
as the volume data (often 2-3 days apart). To allow for specific injectivity calculations,
injection between volume measurements was assumed to be linear and used to estimate the
volume at the time of water level measurement.
However, historical data in Figure 8 shows a declining trend in regional groundwater levels
prior to injection in observation bore GWB0012M (adjacent to HGA0003P). Linear
regression was used to adjust the initial groundwater levels at a rate of -0.76m per year to
account for the regional trend.
A cumulative deviation from mean rainfall (CDFM) analysis was used to determine if the
regional decline in groundwater levels could be attributed to climatic variation.
Anthropogenic causes such as groundwater abstraction, land use changes, vegetation clearing
can also contribute to groundwater level decline. This method has been used in the literature
for similar groundwater investigations by Eakin (1964), Temperley (1980) and Boehmer
(1998). The CDFM is calculated on a monthly basis by subtracting the actual rainfall from the
long term average and plotting the cumulative difference over time. A decreasing trend in the
CDFM plot indicates a period of below average rainfall. The plot is matched to the GWB0012
hydrograph to determine a relationship between climate and groundwater levels.
32
Figure 8 Hydrograph at observation bore GWB0012M showing a declining regional groundwater trend prior to
injection (1997 – 2012)
3.2.3 Well Efficiency
Changes to the well efficiency following the injection trial were analysed to indicate relative
operational performance. Data was sourced from step-drawdown tests conducted by AquaGeo
(2013) and historic step-drawdown data from Woodward-Clyde in 1997-1998. The details of
the step-drawdown tests are summarised in Table 4 below which highlight the differences in
flow rate, duration and number of steps of the „before‟ (1997-1998) and „after‟ (2013) tests.
Table 4 Details of the step-drawdown tests conducted by Woodward-Clyde (1997), Woodward-Clyde (1998) and
AquaGeo (2013)
Bore ID Date Duration (mins)
Steps Discharge Rates (L/s)
Pump Inlet Depth (mbgl)
HGA0001P 18/04/1998 100 5 10, 13. 16, 19, 25 81 12/06/2013 60 4 6, 14, 21, 28 109
HGA0002P 19/04/1998 100 5 10, 13. 16, 19, 25 75 14/06/2013 60 4 8, 14, 21, 28 124
HGA0003P 12/07/1997 100 5 8, 12, 16, 18, 20 100 1/06/2013 60 4 6, 10, 15, 20 106
The step-drawdown test data was analysed using the Hantush-Bierschenk method to
determine the coefficients B and C of the Jacob‟s well equation (Kruseman and de Ridder,
1994):
where
572
574
576
578
580
582
584
586
1997 1999 2001 2003 2005 2007 2009 2011
Gro
undw
ater
Lev
el (m
RL
)Pre-Injection regional groundwater trend (GWB0012M)
33
Sw = drawdown in the production well (m)
Q = constant discharge (m3/d)
B = linear coefficient
C = non-linear coefficient
P = exponent, can vary from 1.5 to 3.5 with 2 widely utilised (Ramey, 1982)
Sw vs time was plotted on semi-log paper to determine ∆Sw(i), the increments of drawdown
between each step by the extrapolation of data. The total drawdown during the n-th step,
Sw(n), was calculated as the sum of the drawdown increments.
Using the discharge during the n-th step, Qn, the ratio of Sw(n)/Qn was calculated and plotted
vs the corresponding values of Qn for each step. The slope of the straight line fitted to the data
points is the coefficient C and the Sw(n)/Qn -axis intercept is the coefficient B.
The well efficiency, Ew, can be defined as the ratio of field specific capacity to theoretical
specific capacity and was calculated using the equation from Kasenow (2006):
As Table 4 shows, the step-drawdown tests conducted for HGA0003P pre- and post-injection
utilised different numbers of steps and different discharge rates per step. In order to achieve
direct comparison of the results, linear regression was applied to the post-injection data of
well efficiency vs discharge data to predict well efficiencies at the pre-injection dataset
discharge rates.
Sensitivity analysis
Ramey (1982) stated that the value of the Jacob‟s well equation exponent, P, can range
between 1.5 and 3.5. A sensitivity analysis was undertaken to determine whether changing the
value of P changed the interpretation of the well efficiency results. An interpretation of the
equation from Kasenow (2006) was used to re-calculate Ew(P), well efficiency as a function of
P:
34
Where P = {1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 3.5}
3.3 Clogging Diagnostics
3.3.1 Graphical Tool
The graphical diagnostic tool outlined in Pyne (2005) was applied to the injection trial data to
give an indication of the potential clogging mechanism if applicable. Figure 7 shows standard
curves for mechanical, biological and physical clogging which are distinctive in terms of the
resistance to flow over time. Flow resistance, or in-well mounding, was plotted over time and
compared to the standard curves. Table 5 show the static water level (SWL) used to calculate
in-well mounding, recorded on the 9/2/12 prior to the commencement of any injection
activities. Consideration for a declining trend in the regional groundwater table, as described
in section Methods: Specific Injectivity, was also included in the analysis.
Table 5 Static Water level for the injection bores recorded on 9/2/12
Injection Bore HGA0001P HGA0002P HGA0003P SWL 9/2/12 577.16mRL 577.13mRL 577.01mRL
3.3.2 Water Quality Analysis
Water quality samples during the injection period were analysed for a range of organic and
inorganic parameters, with focus on turbidity, suspended solids and dissolved iron as
indicators for clogging potential. Martin (2013) indicates that turbidity values less than 5
NTU is a practical target to identify physical clogging. However, this is highly dependent on
the scale of the controlling pores or voids in the aquifer.
3.3.3 Bore Casing Image Analysis
The downhole images of each injection bore‟s inner casing was quantitatively analysed prior
to injection (June 2011), post-injection (May 2013) and following aquifer testing (June 2013)
to determine the impact of the injecting and aquifer testing activities on the proportion of
biofilm on the screens.
The images were captured by Surtech Systems using an optical televiewer (OTV) instrument
which captures at 360° oriented image of the borehole using a digital charge-coupled device
(CCD) camera. The raw image was analysed using Irfan View image processing software
(Version 4.36) at 1m intervals. Firstly, the colour settings at each 1m section were enhanced
35
to identify the biofouling layer as black, an example is shown in Figure 9. The colour settings
used in the analysis varied given the different light settings in the raw images and are
provided in Table C-1, Appendix C. The percentage of biofouling for each 1m interval was
calculated using the red channel of the Image Histogram function, which counts the number
of pixels at a given colour or Index (in this case black = Index 0), as shown in Figure C-1 in
Appendix C.
Raw Image Enhanced Image
Figure 9 An example of a 1m section showing both the raw and enhanced OTV image
3.3.4 Saturation Index
Saturation indicies are used to predict whether a mineral is likely to dissolve or precipitate in
solution (Packhurst and Appelo, 1999). To determine the potential for chemical clogging,
trends in the saturation indices during injection were examined. Prior to the injection trial,
sulphate and carbonate minerals were identified by Barber (2010) as risks to the injection
system and thus have been selected for analysis.
The saturation index, SI, compares the ion activity product from water quality data at a certain
temperature to the equilibrium constant at that same temperature and is calculated according
to (Deutsch, 1997):
Where IAP = ion activity product, from water quality data
KMineral = equilibrium constant for mineral, temperature dependant
36
If SI = 0, the mineral is in equilibrium with solution. If the IAP is greater than the equilibrium
constant (SI>0), the groundwater is oversaturated and the mineral is likely to precipitate. If
the IAP is less than the equilibrium constant (SI < 0), the sample is undersaturated with
respect to the mineral likely to dissolve. However, Deutsch (1997) indicates that the practical
range for equilibrium in groundwater is SI = 0.5.
Table 6 lists the dissolution reactions and ion activity products for carbonates (calcite and
dolomite) and sulphates (gypsum, bartite and anhydrite) included in the analysis.
Table 6 Dissolution reactions and ion activity products (IAP) for minerals included in the analysis (adapted from
Deutsch, 1997)
Mineral Composition Dissolution Reaction IAP=
Calcite CaCO3 CaCO3 Ca2+ + CO32- (aCa2+)(aCO3 2-)
Dolomite CaMg(CO3)2 CaMg(CO3)2 Ca2+ + Mg2+ +
2CO32-
(aCa2+)(aMg2+)(aCO3 2-
)2
Gypsum CaSO4∙2H2O CaSO4∙2H2O Ca2+ + SO4
2- +
2H2O
(aCa2+)(aSO4 2-)
Bartite BaS O4 BaS O4 Ba2+ + SO42- (aBa2+)(aSO4 2-)
Anhydrite CaSO4 CaSO4 Ca2+ + SO42- (aCa2+)(aSO4 2-)
The geochemical modeling program PHREEQC Interactive (Version 3.1.2) with the minteq
thermodynamic database was used to determine the saturation index. Water quality data for
major, minor and trace inorganics was available for fourteen groundwater samples between
July 2010 and October 2013. Eight of these samples had associated field-determined pH and
temperature data. Figure C-2 in Appendix C shows that the laboratory-determined pH is
greater than the field-determined pH, indicating that the sample has reached equilibrium with
CO2 in the atmosphere and degassed prior to laboratory analysis. Therefore, the field-
determined pH better represented in situ conditions and used as the PHREEQC input. For the
eight samples where the field-determined temperature and pH were not available, an average
value was applied from field data collected during the injection period. Figure C-3 in
Appendix C shows there is no trend with field pH with time.
Sensitivity Analysis
37
A sensitivity analysis was undertaken across the temperature range of 25 - 35C at 0.25C
intervals to determine the impact of temperature uncertainty on the interpretation of saturation
index results.
4 Results
4.1 Aquifer Response
4.1.1 Hydrographs
Figure 10, Figure 11 and Figure 12 show the groundwater level and injection volume plots for
HGA0001P, HGA0002P and HGA0003P respectively. HGA0003P shows the least volume of
injected water (green bars) and the greatest water level response (purple line). Conversely,
HGA0001P and HGA0002) show a marginal response to a significant volume of water
injected.
Figure 10 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0001P
0
5000
10000
15000
20000
25000
30000
575
576
577
578
579
580
581
582
583
584
Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13
Vol
ume (
kL) -
Flow
Met
er
Wat
er L
evel
(mR
L) -
Man
ual D
ip
HGA0001P- Groundwater Levels and Injection Volumes
Injection Volum (kL per week) HGA0001P Injection Water Level (mRL)
HGA0001P Static Water Level (mRL) HGA0035M Water Level (mRL)
38
Figure 11 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0002P
Figure 12 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0003P
4.1.2 Mounding
The pre-injection groundwater surface in Figure 13 shows a shallow gradient trending to the
south-east with water levels ranging from 576.8 to 577.1mRL. Figure 14 shows that the post-
injection surface water table ranges from 581.3 to 582.3mRL with elevated water levels above
HGA0001P. The mounding map in Figure 15 shows there is 4.7-5.1m of mounding across the
0
5000
10000
15000
20000
25000
30000
576
578
580
582
584
586
588
Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13
Vol
ume (
kL) -
Flow
Met
er
Wat
er L
evel
(m
RL
) -M
anua
l Dip
HGA0002P - Groundwater Levels and Injection Volumes
Injection Volume (kL per week) HGA0002P Injection Water Level (mRL)
HGA0002P Static Water Level (mRL) HGA0036M Water Level (mRL)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
575
580
585
590
595
600
605
610
615
Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13
Vol
ume (
kL) -
Flow
Met
er
Wat
er L
evel
(m
RL
) -M
anua
l Dip
HGA0003P - Groundwater Levels and Injection Volumes
Injection Volume (kL per week) HGA0003P Injection Water Level (mRL)
HGA0003P Static Water Level (mRL) GWB0012M Water Level (mRL)
39
study area as a result of 12 months of injection with peak mounding around HGA0001P.
Figure 13 Pre-injection groundwater surface in mRL from the 09/02/2012 (0.1m contour intervals)
Figure 14 Post-injection groundwater surface in mRL from the 11/4/2013 (0.1m contour intervals)
40
Figure 15 Groundwater mounding in m following 12-months of injection (0.1m contour intervals)
4.2 Operational Performance
4.2.1 Aquifer vs In-Well Groundwater Level
Figure 16 shows a rapid increase in the groundwater level difference for HGA0003P
indicating poor performance. In contrast, the difference at HGA0001P remains steady over
time and HGA0002P marginally increases over time, indicating performance is slowly
reducing.
Figure 16 Plot of the difference between the injection bore and the closest observation bores groundwater levels
-5.0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13
Diff
eren
ce (m
)
Aquifer vs In-Well Groundwater Level
HGA0001P vs HGA0035M HGA0002P vs HGA0036M HGA0003P vs GWB0012M
Continuous Injection
Testing and Commissioning
41
4.2.2 Specific Injectivity
Figure 17 shows that the long-term specific injectivity decreases over time during the period
of continuous injection. This trend is as expected for decreased operational performance,
suggesting the long term effects of persistent clogging. The results for HGA0001P range from
759.3 to 1667.2 m3/d/m which far exceed the values for HGA0002P (from 338.8 to 637.6
m3/d/m) and HGA0003P (from 20.3 to 49.9 m3/d/m). However, no clear trends are
discernable for the short-term specific injectivity where results span several orders of
magnitude.
The CDFM plot in Figure 19 shows a period of below average rainfall prior to the injection
trial. Figure 20 indicates rainfall has a moderate degree of influence over the regional decline
in groundwater level.
42
Figure 17 Long-term specific Injectivity, SiL over time for each injection bore
y = -3.4326x + 142757R² = 0.7104
0
400
800
1200
1600
2000
Aug-12 Sep-12 Nov-12 Dec-12 Feb-13 Apr-13
Spec
ific
Inje
ctiv
ity (m
3/d/
m)
HGA0001PLong Term Specific Injectivity (m3/d/m)
y = -0.9204x + 38391R² = 0.6881
0
100
200
300
400
500
600
700
Aug-12 Sep-12 Nov-12 Dec-12 Feb-13 Apr-13
Spec
ific
Inje
ctiv
ity (m
3/d/
m)
HGA0002PLong Term Specific Injectivity (m3/d/m)
y = -0.3845x + 15866R² = 0.8208
0
10
20
30
40
50
60
Aug-12 Sep-12 Nov-12 Dec-12 Feb-13 Apr-13
Spec
ific
Inje
ctiv
ity (m
3/d/
m)
HGA0003PLong Term Specific Injectivity (m3/d/m)
43
Figure 18 Short-term specific injectivity, SIS over time for each injection bore
0
40000
80000
120000
160000
200000
Aug-12 Sep-12 Nov-12 Dec-12 Feb-13 Apr-13
Spec
ific
Inje
ctiv
ity (m
3/d/
m)
HGA0001PShort Term Specific Injectivity (m3/d/m)
0
10000
20000
30000
40000
50000
60000
Aug-12 Sep-12 Nov-12 Dec-12 Feb-13 Apr-13
Spec
ific
Inje
ctiv
ity (m
3/d/
m)
HGA0002PShort Term Specific Injectivity (m3/d/m)
0
100
200
300
400
500
600
700
Aug-12 Sep-12 Nov-12 Dec-12 Feb-13 Apr-13
Spec
ific
Inje
ctiv
ity (m
3/d/
m)
HGA0003PShort Term Specific Injectivity (m3/d/m)
44
Figure 19 The cumulative deviation from mean rainfall plot showing a dry period prior to the commencement of the
groundwater injection trial
Figure 20 Matching theCDFM plot with the GWB0012M hydrograph to determine a relationship
4.2.3 Well Efficiency
The Jacob‟s equation coefficients calculated by the Hantush-Bierschenk method are provided
in Table 7 below. The interim steps of the Hantush-Bierschenk method, are provided in
Appendix C – Results.
575
576
577
578
579
580
581
582
583
584
585
-1200
-1000
-800
-600
-400
-200
0
200
400
600
1972 1977 1982 1988 1993 1999 2004 2010
Gro
undw
ater
Lev
el (m
RL
)
CD
FM (m
m)
Regional Groundwater Level and CDFM
CDFM (mm) GWB0012M Groundwater Level
Wet Period Dry Period Wet Period Dry Period
575
576
577
578
579
580
581
582
583
584
585
-350
-150
50
250
450
650
850
1997 1999 2002 2005 2007 2010
grou
ndw
ater
leve
l (m
RL
)
CD
FM (m
m)
Hydrograph and CDFM Matching
CDFM (mm) Water Level (mRL) - Manual Dip
Wet Period Dry Period
45
Table 7 Jacobs equation coefficients determined using the Hantush-Bierschenk method
Injection Bore Test Date B C HGA0001P
Nov 1998 4.35E-04 4.89E-08
June 2013 3.48E-04 1.39E-07
HGA0002P
Nov 1998 1.43E-04 9.30E-08
June 2013 1.61E-04 3.64E-07
HGA0003P
July 1997 3.67E-03 2.13E-06
June 2013 9.16E-03 5.83E-06
Figure 21 shows that the well efficiency is lower after injection and decreases as discharge
increases for all injection bores. HGA0002P was the least efficient performer with Ew ranging
from 64% -15.5%, while the well efficiencies from HGA0001P ranged from 82.2% - 50.5%.
HGA0001P and HGA0002P show a significant (15-30%) reduction well efficiency following
the injection trial in Figure D-3 in Appendix D. Contrastingly the results for HGA0003P show
only a minor change (.5-3.5%).
Figure D-4 in Appendix C shows that the well efficiency, Ew(P), is highly sensitive to changes
in the well equation exponent, P. However, regardless of the value of P, the analysis
systematically shows a decreasing trend in Ew(p) as discharge increases. At the extremes of the
range of P (P>3, P~1.5), the difference between well efficiency at each step becomes
increasingly small as Ew(P) approaches 0 and 100% respectively for al discharge rates.
46
Figure 21 Well efficiency (%) showing results for both pre-injection (dark) and post-injection (light) datasets for
HGA0001P (diamonds), HGA0002) (squares) and HGA0003P (triangles).
4.3 Clogging Diagnostics
4.3.1 Graphical Tool
Figure 22 shows the mounding over time for the three injection bores. The linear rate of
increase in mounding for HGA0001P and HGA0002P could indicate a slow rate of physical
clogging via suspended solid buildup. The results for HGA0003P could indicate either a rapid
suspended solid buildup or mechanical clogging via gas bubbles. However, as injection
ceased in Nov 2012 it is difficult to interpret the results without further data.
4.3.2 Water Quality Analysis
The laboratory-analysed water quality sample results are shown in Table D-2 in Appendix D
with relation to the ANZECC (2000) guidelines for water quality. The water quality
parameters are all within the guideline values, with the exception of pH. As discussed in
Materials and Methods: Saturation Index, the laboratory-determined pH is shown to be
greater than the field-determined pH due to degassing of CO2. Figure C-3 shows that the field
determined pH is within the range outlined in ANZECC (2000). Both the suspended solids
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
500 700 900 1100 1300 1500 1700 1900 2100 2300 2500
Wel
l Eff
icie
ncy
(%)
Flow Rate (m3/d)
Comparison of Well Efficiency
HGA0001P: Pre-Injection (1998) HGA0001P: Post Injection (2013)
HGA0002P: Pre-Injection (1998) HGA0002P: Post Injection (2013)
HGA0003P: Pre-Injection (1997) HGA0003P: Post-Injection (2013)
47
and dissolved iron are below detectible limits, implying a low potential for physical clogging
via suspended sediment buildup and biological/chemical clogging by iron-reducing bacteria.
Figure 23 shows the turbidity results are within the target range (<5 NTU) for 93% of
HGA0001P samples and 85% of HGA0002P samples. This indicates a low to moderate
potential for physical clogging by suspended sediment buildup.
4.3.3 Bore Casing Image Analysis
Figure 24 shows that on average the biofouling layer increased by 48.0% in the slotted PVC
and 57.7% in the wire screen section due to the injection program. Aquifer testing carried out
in June 2013 reduced the biofouling layer by 15.0% and 9.6% respectively. These results are
as expected for decreased performance and are consistent with the results from the Specific
Injectivity and Aquifer Testing analysis. Figure 25 shows the change in biofouling with depth
and indicates the wire wound screen section has the greater tendency to develop a clogging
layer. The pre-injection image and a portion of the post-injection image for HGA0003P was
not able to be analysed due to very poor image quality. Refer to TableD-3 in Appendix D for
further details regarding image quality and thus reliability of biofouling estimates.
Figure 22 Flow resistance in terms of in-well mounding overlain with the standard curves from Pyne (2005).
0
5
10
15
20
25
30
35
40
Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13
Mou
ndin
g (m
)
Flow resistance (in-well mounding) for clogging diagnostics
HGA0001P HGA0002P HGA0003P
Testing and Commissioning Phase
Suspended Solids
Bacterial Growth(Abundant Food Supply)
Bacterial Growth(Limited Food Supply)
Gas bubbles
48
Figure 23 Field measured turbidity for HGA0001P and HGA0002P (HGA0003P not available) showing the target of
<5 NTU outlined by Martin (2013)
Figure 24 Bar chart showing the percentage of biofouling on the slotted PVC screens from the image analysis for the
pre-injection, post-injection and post-test pumping scenarios
0
2
4
6
8
10
12
Nov-12 Feb-13 May-13 Aug-13 Dec-13 Mar-14
Tur
bidi
ty (N
TU
)Field-Determined Turbidity
HGA0001P HGA0002P Target (<5 NTU)
0
10
20
30
40
50
60
70
80
90
100
Slotted Wire Slotted Wire Slotted Wire
HGA0001P HGA0002P HGA0003P
Bio
fou
ling
(%)
Percentage of biofouling by screen type
Pre-Injection: June 2011 Post Injection: May 2013 Post-Pump Test: June 2013
49
Figure 25 Percentage of biofouling with depth for HGA0001P, HGA0002P and HGA0003P showing the slotted PVC and stainless steel wire wound screen sections.
82
84
86
88
90
92
94
96
98
100
102
104
106
108
110
0 25 50 75 100
Dep
th B
elow
Top
Of C
asin
g (m
)
% Biofouling
HGA0001P: Percentage of biofouling with depth
Post-Injection (May 2013)
Post-Test Pumping (June 2013)
Pre-Injection (July 2011)
Slotted PVC
Wire Screen
100
102
104
106
108
110
112
114
116
118
0 25 50 75 100
Dep
th B
elow
Top
Of C
asin
g (m
)
% Biofouling
HGA0002P: Percentage of biofouling with depth
Post-Injection (May 2013)
Post-Test Pumping (June 2013)
Pre-Injection (July 2011)
Wire Screen
Slotted PVC
94
95
96
97
98
99
100
101
102
103
104
105
0 25 50 75 100
Dep
th B
elow
Top
Of C
asin
g (m
)
% Biofouling
HGA0003P: Percentage of biofouling with depth
Post-Injection (May 2013)
Post-Test Pumping (June 2013)
Pre-Injection (July 2011)
Wire Screen
Slotted PVC
50
4.3.4 Saturation Index
The saturation index (SI) results for carbonate minerals shown in Figure 26 indicate the
groundwater in the injection bores are within the equilibrium range for calcite and slightly
saturated for dolomite. Figure 27 indicates that the groundwater is slightly undersaturated
with bartite and moderately undersaturated with anhydrite and gypsum. The sulphate minerals
show a minor decreasing trend as a result of injection. There is no significant change over
time with respect to the mixing of injection waters during the trial. The results are tabulated in
Table D-4 in Appendix D, showing distinction between field-measured pH and temperature
(black) and average pH and temperature (red).
Sensitivity Analysis
Figure D-5 in Appendix D shows that the SIDolomite calculations are most sensitive to changes
in temperature. SIAnhydrite and SIGypsum are robust with respect to temperature, SIBartite and
SICalcite changes by ~0.2 and SIDolomite changes by ~0.5 across the temperature range.
Figure 26 Saturation Index (SI) versus time for carbonate minerals (calcite and dolomite)
-0.5
-0.25
0
0.25
0.5
0.75
1
Jan-10 Jul-10 Feb-11 Aug-11 Mar-12 Sep-12 Apr-13 Nov-13 May-14
Satu
ratio
n In
dex,
SI
Saturation Index: Carbonates
HGA0001P - Calcite HGA0002P - Calcite HGA0003P - Calcite
HGA0001P - Dolomite HGA0002P - Dolomite HGA0003P - Dolomite
Injection Trial
51
Figure 27 Saturation Index (SI) versus time for sulphate minerals (bartite, gypsum and anhydrite)
5 Discussion
5.1 Aquifer Response
The three bores show different responses to the injection trial with respect to in-well water
levels. Marginal localised in-well mounding (~5m) was observed in HGA0001P. This could
be attributed to the cavity intersected by drilling between 115 and 118m (end of hole), the
highly fractured and karstic nature of the Wittenoom dolomite or the bore construction. 18m
of stainless steel wire wound screen was used over the permeable zone in HGA0001P, as
opposed to 12m in HGA0002P and 6m HGA0003P, making it relatively easier for the well to
transmit water into the aquifer. HGA0002P showed ~10m of in well mounding which
progressively accumulated throughout the injection trial. HGA0003 showed a rapid in-well
response to injection (~37m). The screened interval covers both the Wittenoom dolomite
(regional aquifer) and a lower permeability tertiary detrital unit containing high clay content.
The rapid onset of in-well mounding could be attributed to trapped air bubbles in the PVC
screens as a result of turbulent water injected during the testing and commissioning phase.
Alternatively, aquifer compaction due to drilling, poor well development, bore construction or
the high clay content recorded in the top 10m of the dolomite unit could also be contributing
-2.5
-2.25
-2
-1.75
-1.5
-1.25
-1
Jan-10 Jul-10 Feb-11 Aug-11 Mar-12 Sep-12 Apr-13 Nov-13 May-14
Satu
ratio
n In
dex,
SI
Saturation Index: Sulphates
HGA0001P - Gypsum HGA0002P - Gypsum HGA0003P - Gypsum
HGA0001P - Anhydrite HGA0002P - Anhydrite HGA0003P - Anhydrite
HGA0001P - Bartite HGA0002P - Bartite HGA0003P - Bartite
InjectionTrial
Bartite
Gypsum
Anhydrite
52
factors. The static water levels in Figure 10 - Figure 12 (red points), show that the
groundwater returns to pre-trial levels when the system is not operational.
It should be noted that due to their close proximity, the injection bores are not operating in
isolation. A degree of hydraulic connection exists between the injection bores and water level
responses are influenced by the operation of adjacent bores. For example, Figure 12 shows
that the in-well water level at HGA0003P continues to increase after injection in November
2012.
The contour map displaying aquifer mounding as a result of injection shows unlikely water
level peaks over observation bores (away from injection bores). The maps were created in
Surfer using the kriging interpolation method, which seeks to express trends in a dataset and
can interpolate values beyond the range in the raw data (Golden Software Inc, 2012). Despite
this, the use of the kriging method is widely accepted in the industry and in the literature
(Yang et al. 2007; Kambhammettu et al; 2011) and therefore gives a reasonable representation
of the aquifer response to injection.
5.2 Operational Performance
The difference metric plot in Figure 16 further highlights the discrepancies between the
injection bores as discussed above. The large difference between HGA0003P and the adjacent
observational bore GWB0012M is consistent with rapid in-well mounding. The steady
increase in difference metric for HGA0002P could indicate a clogging layer buildup.
However, the adjacent observational bore HGA0036M is not screened in the Wittenoom
dolomite so the apparent reduction in performance could be attributed to a low vertical
connection between the tertiary detritals and the regional dolomite aquifer. Unexpectedly, the
difference metric for HGA0003P was slightly negative (-0.5 to -1m) indicating the water level
is higher in aquifer than inside the injection bore. The adjacent observational bore,
HGA00035M, is screened in the tertiary detrital unit not the Wittenoom dolomite. As this
difference is slight, it could be caused by a survey measurement error for the top of casing
elevation or by perched water hung up in the detrital unit. In order to accurately and
rigorously apply this method to determine bore performance, it is imperative the observational
bores are screened in the same unit as the injection bores
Similarly to specific capacity, the calculation of specific injectivity relies on the assumption
of continuous flow. The analysis was limited to a period of assumed continuous operation by
53
excluding shutdown periods due to the testing and commissioning phase and mechanical
pump failure (~months). However, during the analysis period the system was often
temporarily shutdown (~hours/days) and which has implications for the short-term specific
injectivity calculations. The MAR system is interlinked with mine‟s dust suppression system.
Water carts regularly take water from the same storage dam that supplies the MAR system for
dust suppression purposes, a key safety and environmental hazard on site. During particularly
hot and dusty periods, water managers turn off the MAR system to ensure adequate volumes
of water are available for dust suppression. Measured drawup between consecutive water
level measurements for the short-term specific injectivity calculations were sometimes very
small (<5cm), indicating the system could be in recovery. These small drawups are not
representative of the volume of water injected between measurements and result in extremely
large and unrealistic specific injectivity values.
Due to mine site reporting requirements, field measurements of volume and groundwater
levels were not recorded on the same day. In order to calculate specific injectivity, the volume
of water injected at the time of water level measurement was estimated based on a continuous
flow assumption. As discussed above, this introduces uncertainty into the calculated value.
However, the interpretation of the relative changes in specific injectivity and analysis of
temporal trends is still a valid approach. The long-term specific injectivity decreased during
the study period for all injection bores; HGA0001P by 47.9%, HGA0002P by 58.7% and
HGA0003P by 35.2% (prior to being shut down prematurely). This reduced performance
could be linked to a persistent clogging mechanism or the injection capacity limits of the
regional system. The rate of performance decrease can be used in a management capacity to
plan a tailored maintenance schedule for back flushing and rehabilitation. Based on a
nominated minimum performance threshold, for example 100m3/d/m, the rate of performance
decrease could be used to predict the time period until the threshold is breached for each bore
and thus requires backflushing.
The long-term specific injectivity analysis takes into consideration the regional declining
trend in groundwater levels. As Figure 20 shows, the „dry‟ period of below average rainfall
has a moderate influence over the decline in groundwater levels. However, the decline in
groundwater levels begins before the CDFM dry period, indicating an additional influence on
the regional groundwater system. Water supply from dolomite borefields, dewatering
activities at Rio Tinto‟s Hope Downs mine site from 2006 and at BHP Billiton‟s Mining Area
C from 2010 likely have an influence on groundwater levels.
54
The Hantush-Bierschenk method used to calculate well efficiency is applicable on the basis of
a number of assumptions and conditions. These are listed below in Table 8 with a comment
on their validity for this system. In addition, the method to determine parameters B and C is
based on the extrapolation of drawdown data, therefore introducing a source of uncertainty
(Kruseman and de Ridder, 1994). The sensitivity analysis further highlights the uncertainties
surrounding the well efficiency calculations as changes to the well equation coefficient P have
huge implications on the well efficiency results.
Table 8 Assumptions of the Huntush-Bierschenk method (Kruseman and de Ridder, 1994)
Assumption Remark The aquifer is confined, leaky or unconfined
The regional Wittenoom dolomite aquifer is leaky.
The aquifer has a seemingly infinite extent
The Wittenoom dolomite is restricted in the south due to the outcropping of the Brockman formation and has boundary conditions associated by mine site dewatering activities. However, given the duration of the injection trial this is unlikely to have an effect.
The aquifer is homogenous, isotropic and of uniform thickness
The aquifer is characterized by localised zones of fracturing and weathering. The Wittenoom dolomite pinches out in the south due to the northward dipping stratigraphy.
The piezometric surface is horizontal
The system was unlikely to have fully recovered from injection prior to test pumping so the piezometric surface may not have been horizontal
The aquifer is pumped at increasing discharge rates
The discharge rates increased for each step
The well fully penetrates the aquifer to ensure horizontal flow
The injection bores partially penetrate the Wittenoom dolomite, indicating a vertical flow component near the end of each the well. HGA0002P and HGA0003P are also screened in the tertiary detritals.
The well efficiency calculations in Figure 21 show that the well becomes less efficient at
higher discharge rates due to the turbulent water and the dominance of the well loss
component (CQ2). Compared to HGA0001P, the well efficiency for HGA0002P was
unexpectedly low and could be attributed to the limited use of stainless steel screens in the
bore construction or water inflowing from a narrow fracture zone resulting in high local
velocities. The results for HGA0003P indicate the bore is operating efficiently which is not
consistent with the operational performance analysis. The step-drawdown test for HGA0003P
could have been conducted over lower flow rates than experienced during the injection trial,
which would over estimate its efficiency with respect to injection. Relative to the changes
observed in HGA0001P and HGA0002P, a very small decrease in well efficiency is observed
as a result of injection. This could be attributed to the lower volume of water injected into
55
HGA0003P during the trial or the removal of the bubble lock due to pumping. The „pre-
injection‟ step tests were conducted at the time of drilling, 15 years before the injection trial.
Ideally, the pre-injection testing should be conducted immediately before the injection trial
commences and the effect of the time lag is difficult to separate from the effect of the
injection trial.
5.3 Clogging Diagnostics
The application of the graphical tool used by Pyne (2005) to determine the clogging
mechanism is very limited and subjective. The graphical tool does not account for the
chemical clogging process where precipitation reactions obstruct well viability. However, its
simplicity makes it a quick, easy and practical tool for industry use and would be very
valuable for analysis of future MAR systems within the mining industry.
The clogging layer is easily observable in the bore casing images. Changes to the size of the
clogging layer by drivers such as injection and test pumping are well quantified by the image
analysis. However it is interesting to note that there was a significant portion (~25%) of
clogging layer observed before and unrelated to injection. The method is dependent on high
image quality and this was the limiting factor in analysis for HGA0002P and HGA0003P. The
original light settings on each raw image were different and therefore analysed using different
processing settings as appropriate to enhance the clogging features. This introduces
uncertainty into the analysis, as changes in area of biofilm could be the result of the subjective
change in contrast, brightness and gamma settings. Ideally, the raw images should have the
same light settings and filters in order to undergo the same processing method. Results for
HGA0001P were as expected with an increase the biofilm covering the slotted sections as a
result of injection and a smaller decrease in biofilm as a result of pump testing.
The PHREEQC geochemical modeling results for saturation index rely heavily on the choice
of thermodynamic database which contain equilibrium constant data from a variety of
literature sources (Packhurst and Appelo, 1999). The minteq.dat database was selected for use
as it best replicated previous PHREEQC modeling undertaken by Barber (2010). Todorov et
al. (2006) describe the mindteq.dat database as the “most complete literature database”
available for PHREEQC modeling. Results could vary with a different choice of database. In
addition, uncertainties are derived from the accuracy of chemical analysis in the laboratory,
equilibrium constant data and the method of calculating ion activity products (Deutsch, 1997).
56
Previous geochemical modeling undertaken by Barber (2010) identified the dissolution of
dolomite as a potential risk to the injection trial as a result of mixing source and receiving
waters. The dissolution risk was predicted to reduce if the source waters became partially
aerated prior to injection and reached equilibrium with atmospheric CO2, increasing the
saturation index for dolomite. The modeling results in Figure 26 validate these predictions
and show that SIDolomite is moderately saturated during the injection trial. The groundwater
samples from the injection bores were collected through a tap at the headworks, before the
water was injected into the receiving aquifer. Therefore, the samples represent an „altered‟
form of source water, which has been abstracted from dewatering activities in the open pit,
pumped into a storage dam then pumped again to the MAR system.
The sulphate minerals gypsum, anhydrite and bartite were moderately undersaturated which
theoretically indicates they are likely to dissolve in the aquifer matrix. However, Deutsch
(1997) asserts that an undersaturated mineral in an aquifer scenario can be interpreted as not
being present in the aquifer matrix or not reactive.
6 Conclusions
MAR is an important tool for water managers and, in addition to traditional applications of
water recycling and stormwater harvesting, has huge potential to aid water management in the
mining industry. The MAR system at Mining Area C manages surplus water from BWT
operations and the period from April 2012 to April 2013 was studied. The system returned
excess dewatering supply from localized orebody aquifers to the regional dolomite aquifer via
three injection bores. Clogging at the injection surface through the precipitation of carbonates,
sedimentation and the growth of iron bacteria were identified as risks to the system prior to
operation.
After 12 months of operation, an average 5m of mounding was observed across the study area
with groundwater levels increasing from 577 to 582 mRL. Water levels within the injections
bores exhibited different responses, from 35m maximum mounding in HGA0003P to 2m in
HGA0001P. The in-well water levels returned to static levels when the system was not
operating for brief periods.
The system operational performance was quantified by the difference metric, specific
injectivity and well efficiency and all were shown to decrease throughout the study period.
The specific injectivity decreased for all three bores at differing rates, potentially indicating
57
different flow resistance mechanisms. As expected, the well efficiency decreased for all bores
as a result of injection; a significant decrease at HGA0001P and HGA0002P (~∆25%) and a
minor decrease at HGA0003P (~∆2%). Factors other than clogging, such as well design, bore
construction and localised secondary porosity features could also contribute to the differences
in performance.
The graphical diagnostic tool indicates that HGA0003P is likely affected by rapidly-acting air
entrapment and ongoing sedimentation, while both HGA0001P and HGA0002P by slow
sedimentation or iron bacteria buildup. The growth of iron bacteria is unlikely as the water
chemistry analysis shows the dissolved iron is below the detection limit. The bore casing
image analysis shows that the well screen blockages increased by 48% on the PVC screens
and 58% on the stainless steel wire screens. The test pumping activities only had a minor
remediation effect, removing 9-15% of the clogging layer. The geochemical clogging
potential was investigated with PHREEQC to determine the saturation indices of sulphates
and carbonates. Dolomite was moderately saturated, indicating it has potential to precipitate
and calcite was within the equilibrium range. The groundwater was undersaturated with
respect to sulphates.
The following are recommendations based on learnings from the MAC MAR trial for future
systems in an operational mining environment:
Screen injection bores in only one hydrostratigraphic unit and screen the adjacent
observational bores in the same unit;
Sampling and laboratory analysis of the biofilm layer to determine its composition;
Apply the same step discharge rates for pre- and post aquifer pump testing to increase
confidence in well efficiency comparisons;
Isolate the operation of the MAR system from the dust suppression management
system to validate the assumption of continuous flow which will strengthen
confidence in specific injectivity calculations;
Conduct short-term specific injectivity tests periodically throughout the injection trial
to investigate persistent clogging. Once the system has been operating continuously,
shut down and allows 90% aquifer recovery. The test would measure the short term
response when the system restarts.
58
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A.1
Appendix A – Introduction
A.2
Figure A-1 Location of Flat Rocks rainfall gauge (20km north of Mining Area C), DoW site 505011 (RPS Aquaterra, 2014)
A.3
Figure A-2 Stratigraphic Units of the Hamersley Region (Kneeshaw 2008).
A.4
Figure A-3 Construction bore log for HGA0001 (TP4) from Woodward Clyde (1998)
A.5
Figure A-4 Construction bore log for HGA0002 (TP5) from Woodward Clyde (1998)
A.6
Figure A-5 Construction bore log for HGA0003P (TP3) from Woodward-Clyde (1997)
A.7
Figure A-6 Local 20k geology interpretation at the injection site, A Deposit, Mining Area C..
A.8
Table A-1 Name and description of geology codes used in.
Code Name Description
Cza Alluvium (consolidated) Alluvium - Consolidated & partly clay, silt, sand, gravel in low lying fan deltas and braided flood plains
Qa Alluvium (drainage channels)
Alluvium in drainage channels
Czcb Colluvium & alluvium (chert & BIF clasts)
Colluviuam & Alluvium - Consolidated alluvial fan & slope deposits consisting of clay, silt, soil, sand matrix and pebble to cobble size Chert & BIF clasts
Czc Colluvium & alluvium Colluviuam & Alluvium - Consolidated alluvial fan & slope deposits consisting of clay, silt, soil, sand matrix and pebble to cobble size clasts
PHbw Brockman - Mt Whaleback Shale
Mt Whaleback Shale Member - Ferruginous shale with chert beds. Central chert towards base
PHbd Brockman - Dales Gorge (BIF & Shale)
Dales Gorge Member - BIF and shale (DS 1-16) beds - D2/D3/D4 zones. Basal D1 zone of Chert, BIF and shale (CS 1-6)
AHdb Wittenoom - Bee Gorge Member
Wittenoom Form - Bee Gorge Member - Interbedded dolomite shale, chert and tuff. Turburditic in part
AHmm Marra Mamba - MacLeod Member
Interbedded Shale (MS 1-13) and ferruginous, with podded cherts, some ex carbonate
AHmn Marra Mamba - Mt Newman Member
Podded BIF - carbonate and silicate rich with interbedded shale and carbonate bands (NS 1-8)
AHmu Marra Mamba - Nammuldi Member
Yellow cherty BIF with thin shale interbeds (US 1-18). Carbonate rich in lower half
AHr Mt McRae Shale Mt McRae Shale - Pink buff weathering, pyritic black shale with massive yellow to grey chert interbeds
AHs Mt Sylvia Formation Mt Sylvia Formation - Pink weathering shale, siltstone and chert and 3 BIF markers. Block BIF (6m Brunos Band) at top.
Czcg Indurated colluvium (Canga)
Indurated Colluvium (Canga) consisting of iron ore pebbles in hard vitreous goethite or calcareous matrix
Czl Ferricrete (laterite) Ferricrete (laterite) Czco Colluvium & alluvium (ore
clasts) Colluviuam & Alluvium - Consolidated alluvial fan & slope deposits consisting of clay, silt, soil, sand matrix and pebble to cobble size clasts Ore Clasts
Czcm Colluvium & alluvium (ore, chert & BIF clasts)
Colluviuam & Alluvium - Consolidated alluvial fan & slope deposits consisting of clay, silt, soil, sand matrix and pebble to cobble size clasts Ore, Chert & BIF Clasts
H2 Martite goethitic supergene ore (ochereous)
Martite (Ochreous) goethite supergene iron ore preserving bedrock fabric. Mesozoic/Tertiary in age.
Tmco Alluvial Haematite (red ochre detritals)
Alluvial haematite siltstone/conglomerate (red ochre detritales) in paleochannels. Earl – mid miocene
A.9
Figure A-7 MAR conceptual model (BHP Billiton, 2011)
B.1
Appendix B - Literature Review
B.2
Table B-1 Examples of MAR systems adapted from NWQMS (2009)
MAR System
Type
Description Locality of example
Aquifer Storage and Recovery (ASR)
Injection into a well for storage and recovery from the same well to a confined or unconfined aquifer.
Grange, and Tea Tree Gulley, Adelaide, South Australia
Aquifer Storage, Recovery and Transport (ASTR)
Injection into a well for storage and recovery from a different well for water quality treatment purposes.
Salisbury, South Australia
Vadose Zone Wells
Injection into a dry well to allow infiltration to a deep unconfined aquifer.
Phoenix, United States
Percolation tanks and recharge weirs
Construction of a dam or weir in an ephemeral stream channel to allow infiltration to unconfined aquifers and subsequent recover downstream.
Callide Valley, Queensland
Rainwater harvesting
Diversion of roof runoff into a well or sump filled with sand or gravel.
Perth, Western Australia
Bank filtration Extraction from a well near or under a surface water body to induce infiltration.
Berlin, Germany
Infiltration galleries
Infiltration through geotechnically-stabilised buried trenches to an unconfined aquifer.
Floreat Park, Western Australia
Dune filtration Construction of a pond in a dune to allow infiltration for extraction at lower elevations.
Amsterdam, The Netherlands
Infiltration ponds Construction of a pond or channel off-stream to allow infiltration to an underlying unconfined aquifer;
Burdekin Delta, Queensland
Soil aquifer treatment
Diversion of treated sewage effluent to infiltration ponds.
Alice Springs, Northern Territory
Underground dams
Construction of a trench across an ephemeral streambed, backfilled with low permeability material for flood management purposes.
Northeast Brazil
Sand dams Construction of a sand dam on an ephemeral stream to create an artificial aquifer following periods of inundation.
Kitui, Kenya
Recharge releases Construction of a dam on an ephemeral stream, followed by the slow release of water to promote downstream infiltration.
Little Para River, South Australia
B.3
Figure B-1 Types of MAR schemes (Dillon, 2005)
C.1
Appendix C – Materials and Methods
C.2
Table C-1 Casing Image Analysis: The colour settings used in the downhole camera study for each of the images. Note
the HGA0003P pre-injection image quality was too poor to analyse.
Image Brightness Contrast Gamma HGA0001P: Pre-injection (July 2011) 76 127 0.45 HGA0001P: Post-injection (May 2013) -21 127 0.01 HGA0001P: Post-testing (June 2013) 18 127 0.01 HGA0002P: Pre-injection (July 2011) 81 127 5.39 HGA0002P: Post-injection (May 2013) -122 127 6.99 HGA0002P: Post-testing (June 2013) -122 127 6.99 HGA0003P: Post-injection (May 2013) -21 127 0.01 HGA0003P: Post-testing (June 2013) 18 127 0.01
Figure C-1 Casing Image Analysis: an example of the Image Histogram, which counts the number of pixels of given
colour (Index: Black = 0), to determine the percentage of biofouling.
C.3
Figure C-2 Saturation Index modeling: field-determined pH versus laboratory determined pH
Figure C-3 Saturation Index modeling: field determined pH versus time
7.0
7.5
8.0
8.5
9.0
7.0 7.5 8.0 8.5 9.0
Lab
orat
ory
dete
rmin
ed p
H
Field determined pH
Field pH vs Lab pH
HGA0002P
HGA0003P
HGA0001P
7.0
7.5
8.0
8.5
9.0
Apr-12 May-12 Jul-12 Aug-12 Oct-12 Dec-12 Jan-13 Mar-13
Fiel
d de
term
ined
pH
Field determined pH
D.1
Appendix D - Results
D.2
Figure D-1 The Hantush-Bierschenk method for determining ∆S
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.100
1.200
0.1 1.0 10.0 100.0
Dra
wdo
wn
(m)
Time (Minutes)
HGA0001P Pre-Injection:Hantush-Bierschenk Method for determining ∆s
∆Sw2 = 0.180
∆Sw1 = 0.395
∆Sw3 = 0.105
∆Sw4 = 0.64
∆Sw5 = 0.64
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
0 1 10 100
Dra
wdo
wn
(m)
Time (Minutes)
HGA0001P Post-Injection:Hantush-Bierschenk Method for determining ∆s
∆Sw2 = 0.41
∆Sw1 = 0.22
∆Sw3 = 0.42
∆Sw4 = 0.64
D.3
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.8000.1 1 10 100
Dra
wdo
wn
(m)
Time (Minutes)
HGA0002P Pre-Injection:Hantush-Bierschenk Method for determining ∆s
∆Sw3 = 0.42
∆Sw1 = 0.21
∆Sw4 = 0.68
∆Sw5 = 1.2
∆Sw2= 0
-
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
2.20
2.40 0.1 1 10 100
Dra
wdo
wn
(m)
Time (Minutes)
HGA0002P Post-Injection:Hantush-Bierschenk Method for determining ∆s
∆Sw2 = 0.42
∆Sw1 = 0.3
∆Sw3 = 0.68
∆Sw4 = 1.2
D.4
`
-
2
4
6
8
10
12
14
16 0.1 1 10 100
Dra
wdo
wn
(m)
Time (Minutes)
HGA0003P Pre-Injection:Hantush-Bierschenk Method for determining ∆s
∆Sw2 = 2.35
∆Sw1 = 3.6
∆Sw3 = 3.2
∆Sw4 = 1.75
∆Sw5 = 2.0
-
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00 0.1 1 10 100
Dra
wdo
wn
(m)
Time (Minutes)
HGA0003P Post-Injectoin:Hantush-Bierschenk Method for determining ∆s
∆Sw2 = 6.2
∆Sw1 = 6.3
∆Sw3 = 8.6
∆Sw4 = 12.5
D.5
Table D-1 The Hantush-Bierschenk method for determining Sw(n)/Qn
Step Qn (L/s) Qn (m3/d) ∆Sw (m)
Sw(n) (m)
Sw(n)/Qn (d/m2)
HGA0001P Pre-Injection: ∆t = 100 min 1 10 864 0.40 0.40 0.000458333 2 13 1123 0.18 0.58 0.000512912 3 16 1382 0.11 0.68 0.000492764 4 19 1642 0.19 0.87 0.000530451 5 25 2160 0.28 1.15 0.000530556
HGA0001P Post-Injection: ∆t = 60 min
1 6 518.4 0.22 0.22 0.000424383 2 14 1209.6 0.41 0.63 0.000520833 3 21 1814.4 0.42 1.05 0.000578704 4 28 2419.2 0.64 1.69 0.000698578
HGA0002P Pre-Injection: ∆t = 100 min
1 10 864 0.21 0.21 0.000243056 2 13 1123 0.00 0.21 0.000186999 3 16 1382 0.20 0.41 0.000294501 4 19 1642 0.14 0.55 0.000332521 5 25 2160 0.16 0.70 0.000324537
HGA0002P Post-Injection: ∆t = 60 min
1 8 691.2 0.3 0.3 0.000434028 2 14 1209.6 0.42 0.72 0.000595238 3 21 1814.4 0.68 1.4 0.000771605 4 28 2419.2 1.2 2.6 0.001074735
HGA0003P Pre-Injection: ∆t = 100 min
1 8 691.2 3.6 3.6 0.005208 2 12 1036.8 2.35 5.95 0.005739 3 16 1382.4 3.2 9.15 0.006619 4 18 1555.2 1.75 10.9 0.007009 5 20 1728.0 2 12.9 0.007465
HGA0003P Post-Injection: ∆t = 60 min
1 6 518.4 6.3 6.3 0.012153 2 10 864.0 6.2 12.5 0.014468 3 15 1296.0 8.6 21.1 0.016281 4 20 1728.0 12.5 33.6 0.019444
D.6
Figure D-2 The Hantush-Bierschenk method for determining parameters B and C
4.5E-04
4.6E-04
4.7E-04
4.8E-04
4.9E-04
5.0E-04
5.1E-04
5.2E-04
5.3E-04
5.4E-04
5.5E-04
0 500 1000 1500 2000 2500
S w(n
)/Qn
(d/m
2 )
Q (m3/d)
HGA0001P Pre-Injection:Hantush-Bierschenk method for determining parameters B and C
C = gradient = 4.89 x 10-8
B= y-intercept = 4.35 x 10-4
0.0E+00
1.0E-04
2.0E-04
3.0E-04
4.0E-04
5.0E-04
6.0E-04
7.0E-04
8.0E-04
0 500 1000 1500 2000 2500 3000
S w(n
)/Qn
(d/m
2 )
Q (m3/d)
HGA0001P Post-Injection:Hantush-Bierschenk method for determining parameters B and C
C = gradient = 1.39 x 10-7
B= y-intercept = 3.48 x 10-4
D.7
0.00E+00
5.00E-05
1.00E-04
1.50E-04
2.00E-04
2.50E-04
3.00E-04
3.50E-04
4.00E-04
0 500 1000 1500 2000 2500
S w(n
)/Qn
(d/m
2 )
Q (m3/d)
HGA0002P Pre- Injection:Hantush-Bierschenk method for determining parameters B and C
C= gradient = 9.30 x 10-8
B= y-intercept = 1.43 x 10-4
0.00E+00
2.00E-04
4.00E-04
6.00E-04
8.00E-04
1.00E-03
1.20E-03
0 500 1000 1500 2000 2500 3000
S w(n
)/Qn
(d/m
2 )
Q (m3/d)
HGA0002P Post-Injection:Hantush-Bierschenk method for determining parameters B and C
C = gradient =3.64 x 10-7
D.8
0.00E+00
2.00E-04
4.00E-04
6.00E-04
8.00E-04
1.00E-03
1.20E-03
0 500 1000 1500 2000 2500 3000
S w(n
)/Qn
(d/m
2 )
Q (m3/d)
HGA0002P Post-Injection:Hantush-Bierschenk method for determining parameters B and C
C = gradient =3.64 x 10-7
0.00E+00
5.00E-03
1.00E-02
1.50E-02
2.00E-02
2.50E-02
0 400 800 1200 1600 2000
S w(n
)/Qn
(d/m
2 )
Q (m3/d)
HGA0003P Post-Injection:Hantush-Bierschenk method for determining parameters B and C
C = gradient = 5.85 x 10-6
D.9
Figure D-3 The reduction in well efficiency (EW) between pre-injection and post-injection data
15.9
1%
17.9
1%
20.0
3%
22.2
6% 27.0
1%
29.8
1%
27.0
4%
25.3
6%
24.5
3%
24.6
7%
1.57% 0.45% 1.32% 2.30%3.57%
0%
5%
10%
15%
20%
25%
30%
35%
1 2 3 4 5
Red
uctio
n in
Wel
l Eff
icie
ncy
(Ew
)
Step
Well Efficiency Reduction
HGA0001P HGA0002P HGA0003P
D.10
Figure D-4 Results of the sensitivity analysis where the well efficiency at each step is calculated using a range of P values
0%10%20%30%40%50%60%70%80%90%
100%
1.5 2 2.5 3 3.5
Wel
l Eff
icie
ncy
(Ew)
Exponent (P)
Sensitivity Analysis: HGA0001P (Pre-Injection)
Step 1
Step 2
Step 3
Step 4
Step 5
0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
1.5 2 2.5 3 3.5
Wel
l Eff
icie
ncy
(Ew)
Exponent (P)
Sensitivity Analysis: HGA0001P (Post-Injection)
Step 1
Step 2
Step 3
Step 4
D.11
0%10%20%30%40%50%60%70%80%90%
100%
1.5 2 2.5 3 3.5
Wel
l Eff
icie
ncy
(Ew)
Exponent (P)
Sensitivity Analysis: HGA0002P (Pre-Injection)
Step 1
Step 2
Step 3
Step 4
Step 5
0%10%20%30%40%50%60%70%80%90%
100%
1.5 2 2.5 3 3.5
Wel
l Eff
icie
ncy
(Ew)
Exponent (P)
Sensitivity Analysis: HGA0002P (Post-Injection)
Step 1
Step 2
Step 3
Step 4
0%10%20%30%40%50%60%70%80%90%
100%
1.5 2 2.5 3 3.5
Wel
l Eff
icie
ncy
(Ew)
Exponent (P)
Sensitivity Analysis: HGA0003P (Pre-Injection)
Step 1Step 2Step 3Step 4Step 5
0%10%20%30%40%50%60%70%80%90%
100%
1.5 2 2.5 3 3.5W
ell E
ffic
ienc
y (E
w)
Exponent (P)
Sensitivity Analysis: HGA0003P (Post-injection)
Step 1Step 2Step 3Step 4
D.12
Table D-2 Water quality sample results undertaken during injection with the ANZEC 2000 Guideline values; grey indicates that the analyte is below the detectible limit
Analyte 23-Jun-10 25-Oct-10 13-Dec-12 24-Jan-13 11-Jun-13 12-Jun-13 13-Dec-12 24-Jan-13 15-Jun-13 23-Jun-10 14-Jun-12 9-Jun-13Aluminium (mg/L) 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.055Arsenic (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.007Barium (mg/L) 0.001 0.001 0.008 0.009 0.007 0.006 0.008 0.008 0.009 0.007 0.011 0.005 0.7Bicarbonate Alkalinity (mg/L) 253 258 249Boron (mg/L) 0.19 0.15 0.2 0.22 0.22 0.17 0.2 0.22 0.21 0.27 0.22 0.37Cadmium (mg/L) 0.0001 0.0001 0.001 0.0001 0.0001 0.0001 0.001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0002^Calcium (mg/L) 68 59 51 34 49 48 48 37 51 63 41 50Chloride (mg/L) 43 46 42 40 34 34 42 38 37 51 42 37 250Chromium (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001Copper (mg/L) 0.001 0.001 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.001 0.0014^Fluoride (mg/L) 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.3 1.5Iron Sol. (mg/L) 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.3Lead (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.0034^Magnesium (mg/L) 42 39 34 33 33 32 34 31 35 39 34 35Manganese (mg/L) 0.001 0.001 0.002 0.001 0.001 0.038 0.001 0.001 0.042 0.001 0.001 0.001 1.9Mercury (mg/L) 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.00006Molybdenum (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.05Nickel (mg/L) 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.028 0.013 0.001 0.011^pH (pH) 8.01 8.06 8.32 8.11 8.02 8.07 8.29 8.23 7.92 8.01 8.05 8.05 6.5-8.0^^^Potassium (mg/L) 10 9 10 8 9 8 10 9 9 11 9 9Selenium (mg/L) 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.005Silica (mg/L) 38.2 34.1 30.6 28.7 32.1 32.1 30 27 31.7 34 22.2 33.2Sodium (mg/L) 36 33 67 33 33 32 36 33 34 37 35 35 180Sulphate as SO4 2- (mg/L) 49 43 49 40 42 42 47 39 44 52 43 45 500Suspended Solids (SS) (mg/L) 1 5 5 22 5 5 5 5 5 5Total Dissolved Solids(mg/L) 484 454 372 323 330 288 400 339 358 435 351 351 500Zinc (mg/L) 0.005 0.005 0.056 0.005 0.007 0.005 0.035 0.005 0.005 0.005 0.005 0.005 0.008
Notes for ANZECC 2000 Fresh Water Guidelines for slightly-moderately disturbed ecosystems Grey text Below detectible limit^ Guideline is based on a hardness (CaCO3) of 30 mg/L. ^^ This range is for upland and lowland rivers in South-west Australia^^^ Denotes value for a lowland river in South-west Australia
ANZECC 2000 Guidelines
HGA0001P HGA0002P HGA0003P
D.13
Table D-3 Image quality estimates and comments on image reliability for the downhole camera study for each 1m section of HGA0001P, HGA0002P and HGA0003P.
HGA0001P Depth
From (m)
Depth To (m)
Casing Type
Pre-Injection (July 2011) Post-Injection (May 2013) Post-Test Pumping (June 2013) Quality
Estimate Comments Quality Estimate Comments Quality
Estimate Comments
82 83 S G Depth scale of image out with respect to the May 2013 image.
A G Depth scale of image out with respect to the May 2013 image. 83 84 S G A
Under estimate do to removal of biofilm layer from OTV tool. "Stripes"
G 84 85 S G Image is clear, no biofouling. A G
Biofouling layer doesn‟t cover slots, just blank PVC 85 86 S G
Analysis by visual inspection
A G 86 87 S G A G 87 88 S G P Not useable, contains join P Not useable, contains join 88 89 S G G A Different image, light filter
looks different 89 90 S G G A 90 91 S G G A Biofouling layer looks to be
decreasing 91 92 S G G A 92 93 S G G A Screen looking much clearer 93 94 S G G A Over estimate, contains join. 94 95 S G G A Screen looks very clear 95 96 S G G A 96 97 S G G A 97 98 S G G A 98 99 S G G A 99 100 S P Not useable, contains join P Not useable, contains join P Not useable, contains join
100 101 W G Some 'floaties'; turn red not counted in biofilm
A
Biofouling covers most of screen. Image slightly unclear but still useable
P Two different light settings 101 102 W G A A 102 103 W A
A A
103 104 W A
A A 104 105 W A
Floaties in water casting a shadow and cover the screen.
A A 105 106 W A A A 106 107 W A P Image gets quite dark and
un useable from 106 - 112m. Looks like biofouling increases to full depth. Use the average as an estimate.
A 107 108 W A P A 108 109 W P Warped but still useable P A 109 110 W P As above. 109.4-110.4m P P
Image is getting quite dark, almost unuseable 110 111 W P
Short section to the end of the screen P P
D.14
HGA0002P
Depth From (m)
Depth To (m)
Casing Type
Pre-Injection (July 2011) Post-Injection (May 2013) Post-Test Pumping (June 2013) Quality
Estimate Comments Quality Estimate Comments Quality
Estimate Comments
100 101 S A Image is clear P Includes the join section A Includes the join section 101 102 S A
Analysis by visual inspection
P A 102 103 S A P A 103 104 S A P A 104 105 S A P Image very light A 105 106 S P
P A
106 107 S P Not useable, contains join P Not useable, contains join A Not useable, contains join 107 108 S P
P A
108 109 S P Dark streak in image (not biofilm), therefore over estimate
P A "Stripes" 109 110 S P P A Very pixilated 110 111 S P P Image is pixilated, however
biofouling is still observable A
111 112 S P P A Very light 112 113 S P Not useable, contains join P Not useable, contains join P Not useable, contains join 113 114 W P Very dark P Pixilated A Slightly distorted 114 115 W P A Still distorted but better A Sharper than above 115 116 W P Not in focus A A Slightly distorted 116 117 W P Image too dark and distorted to
use effectively; assume average for the remainder of the screened section
A A
117 118 W P G Image is much sharper G Used as an example in write up
118 119 W P G End of OTV survey G End of OTV survey
D.15
HGA0003P Depth
From (m)
Depth To (m)
Casing Type
Pre-Injection (July 2011) Post-Injection (May 2013) Post-Test Pumping (June 2013) Quality
Estimate Comments Quality Estimate Comments Quality
Estimate Comments
94 95 S P
Image blurry and out of focus
P
Image not useable due to light settings: too dark then too light
G
No biofilm; slots are clear on PVC
95 96 S P P G 96 97 S P P G 97 98 S P P G 98 99 S P P G 99 100 S P P P Not useable, contains join
100 101 W P
Image difficult to process due to particulates floating in the water.
A Raw image looks like more biofouling than shows up in the enhanced image
A Particulates floating in the water, creates an over estimate
101 102 W P A A 102 103 W P A A 103 104 W P A A 104 105 W P A A Different light settings
Image quality scale Description G Good The image is clear and sharp; the biofouling layer is easily distinguishable from the screen A Acceptable The image is reasonably clear; particulates suspended in the water affect screen visibility P Poor The image is distorted or warped; the colouring is too dark or too light
Casing Type S Slotted PVC 1mm aperture
W Wirewound stainless steel screen
D.16
Table D-4 Key inputs and outputs for the PHREEQC modeling (red = average values used in the absence of measured values) IN
PUT
Bore HGA0001P HGA0002P HGA0003P Date 23/6/10 25/10/10 13/12/12 24/1/13 11/6/13 13/6/13 22/10/13 13/12/12 24/1/13 15/6/13 22/10/13 23/6/10 14/6/12 9/6/13 Temperature 31 32.7 30.0 28.5 30.0 30.0 28.3 29.8 28.6 29.8 27.7 31.0 29.9 30.0 pH (field) 7.2 7.1 7.6 7.9 7.6 7.6 7.6 7.7 7.9 7.7 7.4 7.2 7.6 7.4 log pCO2 -1.77 -1.65 -2.34 -2.61 -2.37 -2.38 -2.36 -2.35 -2.60 -2.34 -2.16 -1.99 -2.22 -2.03
OU
TPU
T
Modelled pH 7.20021 7.10198 7.70208 7.90806 7.70014 7.70212 7.60867 7.70867 7.90056 7.70976 7.40789 7.20471 7.60298 7.40729 SI_Anhydrite -1.8744 -1.9611 -1.9775 -2.2082 -2.0384 -2.0427 -2.1886 -2.0089 -2.1817 -2.0146 -2.1994 -1.8725 -2.1024 -2.0099 SI_Bartite N/A N/A -1.0738 -1.0419 -1.177 -1.2392 -1.0569 -1.0725 -1.1036 -1.0542 -0.9986 -1.1428 -0.9657 -1.3033 SI_Calcite 0.0182 -0.0996 0.3235 0.294 0.2843 0.2708 -0.0096 0.3046 0.3264 0.3435 -0.225 -0.0151 0.1606 0.0431 SI_Dolomite 0.1356 -0.0637 0.7748 0.8706 0.7003 0.6689 0.1527 0.7618 0.8714 0.8259 -0.2855 0.0701 0.5432 0.2344 SI_Gypsum -1.9595 -2.0627 -2.0531 -2.2694 -2.114 -2.1183 -2.2474 -2.0821 -2.243 -2.0878 -2.2533 -1.9576 -2.178 -2.0855
D.17
Figure D-5 Sensitivity Analysis for Saturation Index (SI) with respect to temperature for carbonates and sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P23/06/2010 - Carbonates
Anhydrite
Gypsum-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P23/06/2010 - Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P25/06/2010 - Carbonates
Anhydrite
Gypsum-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P25/06/2010 - Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P13/12/2012 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P13/12/2012 - Sulphates
D.18
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P24/01/2013 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P24/01/2013- Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P11/06/2013 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P11/06/2013- Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P13/06/2013 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P13/06/2013- Sulphates
D.19
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P22/10/2013 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA001P22/10/2013 - Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA002P13/12/2012 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA002P13/12/2012 - Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA002P24/01/2013 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA002P24/01/2013 - Sulphates
D.20
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA002P15/06/2013 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA002P15/06/2013 - Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA002P22/10/2013 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA002P22/10/2013 - Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA003P23/06/2010 - Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA003P23/06/2010 - Sulphates
D.21
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA003P14/06/2012- Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA003P14/06/2012- Sulphates
Calcite
Dolomite
-0.5
0
0.5
1
1.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA003P09/06/2013- Carbonates
Anhydrite
Gypsum
Barite
-2.5
-2
-1.5
-1
-0.5
25 27 29 31 33 35
Satu
ratio
n In
dex
(SI)
Temperature ( C)
Sensitivity Analysis for HGA003P09/06/2013 - Sulphates
E.1
Appendix E – Research Proposal
E.2
Clogging mechanisms in Managed Aquifer Recharge: a case study at Mining Area C SCIE9722 FNAS Master of Science Thesis
Clogging mechanisms in Managed Aquifer Recharge: a case study at Mining Area C SCIE9722 FNAS Master of Science Thesis
Lily Smith 208262
Lily Smith 21224043 October 2012
E.3
Cover Photo
The Pilbara near Mining Area C. Photo by author.
Abbreviations
AOC Assimilable Organic Carbon
ASR Aquifer Storage and Recovery
ASTR Aquifer Storage, Transport and Recovery
BIF Banded Iron Formation
BOM Bureau of Meterology
BWT Below Water Table
DEC Department of Conservation
DOC Dissolved Organic Carbon
FMG Fortescue Metals Group
GDE Groundwater Dependent Ecosystems
IGRAC International Groundwater Resource Assessment Centre
MAC Mining Area C
MAR Managed Aquifer Recharge
mbgl meters below ground level
MFI Membrane Filtration Index
NWQMS National Water Quality Management Strategy
ORP Oxidation-Reduction Potential
PFI Parallel Filter Index
PHREEQC PH (pH), RE (redox), EQ (equilibrium), C (programming language)
PVC Polyvinyl chloride
SWL Static Water Level
TD Tertiary Detritals
TOC Total Organic Carbon
TSS Total Suspended Solids
VWP Vibrating Wire Piezometer
E.4
List of Figures
Figure 1 Mining Area C location and site layout, showing the components of the MAR
system (Figure created by author)............................................................................................ 14
Figure 2 MAR schematic diagram showing the seven system components of an MAR project
outlined by Dillon et al. (2009). ............................................................................................... 16
Figure 3 Historic monthly rainfall record at Flat Rocks gauge, 20km north of MAC ............ 17
Figure 4 Geological cross section for HGA0001P looking east along 709317E (Central
Pilbara Grid), unit codes from MAC is located in the Fortescue River basin within the Weeli
Wolli Creek system. The regional groundwater system flows to the east towards Weeli Wolli
Spring, Prior to mining, groundwater levels at MAC were 662mRL and decreasing to
555mRL at Weeli Wolli Spring (Golder, 2011). The convergence of groundwater flow with
outcropping basement rock in the east causes groundwater levels to rise and form the Weeli
Wolli Spring (RPS Aquaterra, 2008). ...................................................................................... 18
Figure 5 Geological cross section for HGA0002P looking east along 709562E (Central
Pilbara Grid) , unit codes from MAC is located in the Fortescue River basin within the Weeli
Wolli Creek system. The regional groundwater system flows to the east towards Weeli Wolli
Spring, Prior to mining, groundwater levels at MAC were 662mRL and decreasing to
555mRL at Weeli Wolli Spring (Golder, 2011). The convergence of groundwater flow with
outcropping basement rock in the east causes groundwater levels to rise and form the Weeli
Wolli Spring (RPS Aquaterra, 2008). ...................................................................................... 18
Figure 6 Geological cross section for HGA0002P looking east along 709770 (Central Pilbara
Grid), , unit codes from MAC is located in the Fortescue River basin within the Weeli Wolli
Creek system. The regional groundwater system flows to the east towards Weeli Wolli
Spring, Prior to mining, groundwater levels at MAC were 662mRL and decreasing to
555mRL at Weeli Wolli Spring (Golder, 2011). The convergence of groundwater flow with
outcropping basement rock in the east causes groundwater levels to rise and form the Weeli
Wolli Spring (RPS Aquaterra, 2008). ...................................................................................... 19
Figure 7 A graphical diagnostic tool for determining different mechanisms of clogging (Pyne,
2005) ........................................................................................................................................ 24
E.5
Figure 8 Hydrograph at observation bore GWB0012M showing a declining regional
groundwater trend prior to injection (1997 – 2012)................................................................. 32
Figure 9 An example of a 1m section showing both the raw and enhanced OTV image ........ 35
Figure 10 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0001P 37
Figure 11 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0002P38
Figure 12 Groundwater levels (mRL) and injection volumes (kL per week) for HGA0003P 38
Figure 13 Pre-injection groundwater surface in mRL from the 09/02/2012 (0.1m contour
intervals) .................................................................................................................................. 39
Figure 14 Post-injection groundwater surface in mRL from the 11/4/2013 (0.1m contour
intervals) .................................................................................................................................. 39
Figure 15 Groundwater mounding in m following 12-months of injection (0.1m contour
intervals) .................................................................................................................................. 40
Figure 16 Plot of the difference between the injection bore and the closest observation bores
groundwater levels ................................................................................................................... 40
Figure 17 Long-term specific Injectivity, SiL over time for each injection bore ..................... 42
Figure 18 Short-term specific injectivity, SIS over time for each injection bore ..................... 43
Figure 19 The cumulative deviation from mean rainfall plot showing a dry period prior to the
commencement of the groundwater injection trial .................................................................. 44
Figure 20 Matching theCDFM plot with the GWB0012M hydrograph to determine a
relationship ............................................................................................................................... 44
Figure 21 Well efficiency (%) showing results for both pre-injection (dark) and post-injection
(light) datasets for HGA0001P (diamonds), HGA0002) (squares) and HGA0003P (triangles).
.................................................................................................................................................. 46
Figure 22 Flow resistance in terms of in-well mounding overlain with the standard curves
from Pyne (2005). .................................................................................................................... 47
E.6
Figure 23 Field measured turbidity for HGA0001P and HGA0002P (HGA0003P not
available) showing the target of <5 NTU outlined by Martin (2013) ...................................... 48
Figure 24 Bar chart showing the percentage of biofouling on the slotted PVC screens from
the image analysis for the pre-injection, post-injection and post-test pumping scenarios ....... 48
Figure 25 Percentage of biofouling with depth for HGA0001P, HGA0002P and HGA0003P
showing the slotted PVC and stainless steel wire wound screen sections. .............................. 49
Figure 26 Saturation Index (SI) versus time for carbonate minerals (calcite and dolomite) ... 50
Figure 27 Saturation Index (SI) versus time for sulphate minerals (bartite, gypsum and
anhydrite) ................................................................................................................................. 51
Figure 28 Types of MAR schemes (Dillon, 2005) .............................................................. E.33
Figure 29 Map of Managed Aquifer Recharge in Australia as of 2011 (Ward and Dillon,
2012) .................................................................................................................................... E.33
Figure 30 Global application of artificial recharge (IGRAC, 2012) .................................... E.34
Figure 31 A graphical diagnostic tool for determining different mechanisms of clogging
(Pyne, 2005) ......................................................................................................................... E.34
Figure 32 Apparatus for a membrane filter test used to determine MFI (Dillon et al., 2001)
.............................................................................................................................................. E.35
Figure 33 MAR Monitoring Network at MAC .................................................................... E.36
Figure 34 Units of the Hamersley Region (Kneeshaw 2008). ............................................. E-37
List of Tables
Table 1 MAR bore details (Refer to MAC is located in the Fortescue River basin within the
Weeli Wolli Creek system. The regional groundwater system flows to the east towards Weeli
Wolli Spring, Prior to mining, groundwater levels at MAC were 662mRL and decreasing to
555mRL at Weeli Wolli Spring (Golder, 2011). The convergence of groundwater flow with
E.7
outcropping basement rock in the east causes groundwater levels to rise and form the Weeli
Wolli Spring (RPS Aquaterra, 2008). ...................................................................................... 15
Table 2 Geological reference table for unit codes ................................................................... 20
Table 3 Static Water level for the injection bores recorded on 9/2/12 .................................... 31
Table 4 Details of the step-drawdown tests conducted by Woodward-Clyde (1997),
Woodward-Clyde (1998) and AquaGeo (2013) ...................................................................... 32
Table 5 Static Water level for the injection bores recorded on 9/2/12 .................................... 34
Table 6 Dissolution reactions and ion activity products (IAP) for minerals included in the
analysis (adapted from Deutsch, 1997) .................................................................................... 36
Table 7 Jacobs equation coefficients determined using the Hantush-Bierschenk method ...... 45
Table 8 Assumptions of the Huntush-Bierschenk method (Kruseman and de Ridder, 1994) . 54
Table 10 Examples of MAR systems adapted from NWQMS (2009) ................................ E-38
Table 11 Summary Table of the MAR Monitoring Bore Network ..................................... E-39
E.8
Abstract
Managed Aquifer Recharge or MAR is a well-established method, useful in the development
of sustainable water management practices. The changing climate, growing population,
effects of urbanisation and surface water scarcity increase pressure on future global water
resources. MAR can help maximise abstraction from groundwater resources while mininsing
environmental impacts. While it has been primarily utilized for potable and agricultural water
supplies in Australia, it has the potential to form an integral part of mine site water
management. It is an effective and sustainable method for disposing of surplus water in an
operational mining capacity and can reduce the long term dewatering footprint of below
water table deposits. Clogging of injection well and the surrounding aquifer matrix is a
common operational issue that results in reduced permeability of injection surfaces. Clogging
can occur via physical, chemical or biological mechanisms or a combination thereof.
Remedial options such as bore redevelopment and pre-treatment of injection water are
available to system operators to manage the effects of clogging. A study is proposed to
determine the clogging potential and redevelopment frequency of a MAR scheme at BHP
Billiton‟s Mining Area C in the Pilbara.
Key words: Managed Aquifer Recharge, clogging, mining, Pilbara
E.9
Table of Contents
List of Figures .......................................................................................................................... vi
List of Tables ........................................................................................................................... ix
Abbreviations ........................................................................................................................... x
Abstract ................................................................................................................................... 11
1 Introduction .................................................................................................................... 12
1.1 Overview .................................................................................................................. 12
1.2 Aims ......................................................................................................................... 13
1.3 System Characterisation........................................................................................... 13
1.3.1 Site Description .................................................................................................... 13
1.3.2 MAR System Characterisation ............................................................................. 14
1.3.3 Climate Characterisation..................................................................................... 16
1.3.4 Hydrogeological Characterisation ...................................................................... 17
2 Literature Review .......................................................................................................... 21
2.1 Managed Aquifer Recharge ..................................................................................... 21
2.1.1 Definition ............................................................................................................. 21
2.1.2 Purpose ................................................................................................................ 21
2.1.3 Types .................................................................................................................... 22
2.1.4 Benefits ................................................................................................................. 22
2.2 Clogging ................................................................................................................... 22
2.2.1 Introduction.......................................................................................................... 23
2.2.2 Types and Causes ................................................................................................. 23
2.2.3 Management Options ........................................................................................... 25
2.2.4 Diagnostic Tools .................................................................................................. 25
2.3 Application of MAR in the Mining Industry ........................................................... 26
2.3.1 Mining Water Management ................................................................................. 26
2.3.2 MAR Schemes in the Pilbara Region ................................................................... 28
2.3.3 Mining Specific Operational Considerations ...................................................... 28
3 Materials and methods .................................................................................................. 29
3.1 Aquifer Response ..................................................................................................... 29
3.1.1 Hydrographs ........................................................................................................ 29
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3.1.2 Mounding ............................................................................................................. 29
3.2 Operational Performance ......................................................................................... 29
3.2.1 Aquifer vs In-Well Groundwater Level ................................................................ 29
3.2.2 Specific Injectivity ................................................................................................ 30
3.2.3 Well Efficiency ..................................................................................................... 32
3.3 Clogging Diagnostics ............................................................................................... 34
3.3.1 Graphical Tool ..................................................................................................... 34
3.3.2 Water Quality Analysis ........................................................................................ 34
3.3.3 Bore Casing Image Analysis ................................................................................ 34
3.3.4 Saturation Index ................................................................................................... 35
4 Results ............................................................................................................................. 37
4.1 Aquifer Response ..................................................................................................... 37
4.1.1 Hydrographs ........................................................................................................ 37
4.1.2 Mounding ............................................................................................................. 38
4.2 Operational Performance ......................................................................................... 40
4.2.1 Aquifer vs In-Well Groundwater Level ................................................................ 40
4.2.2 Specific Injectivity ................................................................................................ 41
4.2.3 Well Efficiency ..................................................................................................... 44
4.3 Clogging Diagnostics ............................................................................................... 46
4.3.1 Graphical Tool ..................................................................................................... 46
4.3.2 Water Quality Analysis ........................................................................................ 46
4.3.3 Bore Casing Image Analysis ................................................................................ 47
4.3.4 Saturation Index ................................................................................................... 50
5 Discussion........................................................................................................................ 51
5.1 Aquifer Response ..................................................................................................... 51
5.2 Operational Performance ......................................................................................... 52
5.3 Clogging Diagnostics ............................................................................................... 55
6 Conclusions ..................................................................................................................... 56
7 References ....................................................................................................................... 58
Abstract .............................................................................................................................. E.8
1 Literature Review ....................................................................................................... E.13
1.1 Introduction .......................................................................................................... E.13
E.11
1.2 Managed Aquifer Recharge ................................................................................. E.13
1.2.1 Definition ......................................................................................................... E.13
1.2.2 Purpose ............................................................................................................ E.14
1.2.3 Types ................................................................................................................ E.14
1.2.4 Benefits ............................................................................................................. E.15
1.3 Clogging ............................................................................................................... E.16
1.3.1 Introduction...................................................................................................... E.16
1.3.2 Types and Causes ............................................................................................. E.16
1.3.3 Management options ........................................................................................ E.18
1.3.4 Diagnostic tools ............................................................................................... E.18
1.4 Application of MAR in the Mining Industry ....................................................... E.20
1.4.1 Water Management in Mining ......................................................................... E.20
1.4.2 MAR Schemes in the Pilbara Region ............................................................... E.21
1.4.3 Mining Specific Operational Considerations .................................................. E.21
1.5 Conclusion ........................................................................................................... E.22
2 Project Proposal .......................................................................................................... E.23
2.1 Title ...................................................................................................................... E.23
2.2 Investigator .......................................................................................................... E.23
2.3 Introductory Statement......................................................................................... E.23
2.4 Objective .............................................................................................................. E.23
2.5 Background .......................................................................................................... E.23
2.6 Significance.......................................................................................................... E.24
2.7 Methodology ........................................................................................................ E.24
2.8 Budget .................................................................................................................. E.26
2.9 Timetable ............................................................................................................. E.26
3 References .................................................................................................................... E.27
Figures .................................................................................................................................. E.33
Tables .................................................................................................................................. E-38
Appendix 1 ........................................................................................................................... E.41
Appendix 2 ........................................................................................................................... E.44
Appendix 3 ........................................................................................................................... E.45
E.12
E.13
1 Literature Review
1.1 Introduction
MAR is the process of enhancing natural rates of recharge to groundwater systems and is
becoming an increasingly important water management tool globally. Dillon (2005) states
that MAR has the potential to be a major contributor to the UN Millennium Goal for water
supply for villages in developing nations. MAR schemes can take on a number of different
forms, with the Aquifer Storage and Recovery (ASR) and infiltration basin type being the
most common. MAR schemes have been successfully implemented in Australia and around
the world for the purposes of stormwater harvesting and wastewater recycling to secure
additional water supplies and maintain environmental outcomes. However, there is huge
potential for MAR to be applied to the mining industry to aid mine site water management.
Clogging at the injection surface poses one of the most challenging and persistent technical
issues for the operation of an MAR scheme (Dillon et al. 2001). Clogging can occur via
physical, chemical and biological processes and several diagnostic methods exist to predict
the clogging potential. An understanding of the clogging formation process and potential in
each MAR system enables efficient operation and maximises the longevity of MAR
infrastructure. This literature review investigates the practicalities of MAR in the mining
industry and the potential for MAR to be applied in the Pilbara mining region of Western
Australia.
1.2 Managed Aquifer Recharge
1.2.1 Definition
Managed aquifer recharge is the “intentional recharge of water to aquifers for subsequent
recovery or environmental benefit” (NWQMS 2009 pg 13). The process has also been
referred to as enhanced recharge, water banking and sustainable underground storage in the
literature (Dillon 2005). The definition specifies „intentional‟ recharge to separate MAR from
incidental or unintended recharge processes such as the effects of land clearing, over
irrigation and increased runoff from urbanisation. Recently, the term “artificial recharge” has
been effectively replaced with MAR to avoid the negative connotations associated with the
perceived unnatural and non-sustainable process.
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1.2.2 Purpose
MAR systems may be implemented for a number of purposes:
to harvest urban stormwater to supplement water resources (Page et al. 2011;
Vanderzalm et al. 2010; Dillon et al. 1999);
to reclaim wastewater to supplement water resources (Page et al. 2010; Bosher et al.
1998; Asano and Levine 1998);
to sustain environmental flows and phreatophytic vegetation (Naumburg et al. 2005);
and to act as a barrier to prevent saline intrusion (Daher et al. 2011; Shammas 2008).
The use of MAR to capture and store stormwater to reduce demand on conventional water
resources has become increasingly popular in Australia over the last decade. Figure 29 from
Ward and Dillon (2012) shows a map of major MAR schemes by type around Australia The
largest Australian MAR project is located in Queensland‟s Burdekin Delta, where recharge of
100GL/year via infiltrations ponds maintains sugar cane production. MAR initiatives have
been implemented with success worldwide, with India, USA, Sweden, Finland, New Zealand,
France and Germany leading the way with application of groundwater recharge management
schemes as shown in Figure 30 (IGRAC 2012).
1.2.3 Types
MAR encompasses a wide variety of water management systems, which vary with recharge
method, source of recharge water, end use of recovered water, scale and complexity. Aquifers
may be recharged by two methods: (a) the injection of source water directly into the target
aquifer through screened wells, or (b) the infiltration of source water through open basins,
galleries or channels. Recharge water may be sourced from drinking water treatment plants,
sewage treatment plants, harvested storm water, irrigation districts, ephemeral streams or
industrial specific sources (Bouwer 2002).
Types of MAR systems are listed below from Tuinhof and Heederik (2003), NWQMS (2009)
and Dillon (2005):
Aquifer Storage and Recovery (ASR): Injection into a well for storage and recovery
from the same well in either a confined or unconfined aquifer;
Aquifer Storage, Transport and Recovery (ASTR): Injection into a well for storage
and recovery from a different well;
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Vadose Zone wells: Injection into a dry well to allow infiltration to a deep underlying
unconfined aquifer;
Percolation tanks and recharge weirs: Construction of a dam or weir in an
ephemeral stream channel to allow infiltration to underlying unconfined aquifers and
subsequent recovery downstream;
Rainwater harvesting: Diversion of roof runoff into a well or sump filled with sand
or gravel;
Bank filtration: Extraction from a well near or under a surface water body to induce
infiltration;
Infiltration galleries: Construction of geotechnically- stabilised buried trenches to
allow infiltration to an underlying unconfined aquifer;
Dune filtration: Construction of a pond in a dune to allow infiltration for extraction
at lower elevations;
Infiltration ponds: Construction of a pond or channel off-stream to allow infiltration
to an underlying unconfined aquifer;
Soil aquifer treatment: Diversion of treated sewage effluent to infiltration ponds for
water quality treatment purposes;
Underground dams: Construction of a trench across an ephemeral stream bed,
backfilled with low permeability material for flood management purposes;
Sand dams: Construction of a sand dam on an ephemeral stream to create an artificial
aquifer following periods of inundation.
Recharge releases: Construction of a dam on an ephemeral stream, followed by the
slow release of water to promote downstream infiltration.
An example of each type of MAR systems is given in Table 9. For a full description of each
scheme refer to page 15 of NWQMS (2009). Figure 28 (Dillon 2005) shows a schematic of
several types of MAR schemes listed above. The ASR type of MAR scheme shall be the
focus of this literature review, as it is the model for the case study at Mining Area C.
1.2.4 Benefits
ASR has many benefits over surface water storages structures such as dams and reservoirs
making it an efficient option for long-term water storage. These include (NWQMS 2009;
Pyne 2006; Bouwer 2002; Kimrey 1989):
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Low capital installation costs;
Low evaporation loss from the aquifer;
Multi-purpose capacity for water quality treatment in addition to storage;
Reduced project area footprint;
Low potential for structural failure (i.e. dam wall failure);
Reduced potential for mosquito habitat;
Flexible system size to meet incremental growth in water demand;
Reduced potential for pollution or damage by sabotage or other hostile action;
Improved reliability of existing supplies
While the benefits of ASR have been widely publicised, the ongoing energy requirements and
operational cost of maintaining an ASR scheme is seldom touched on in the literature.
1.3 Clogging
1.3.1 Introduction
A common operational issue affecting ASR schemes is clogging of the recharge surface,
gravel pack or surrounding aquifer matrix that, in serious cases, can lead to the abandonment
of projects. Also known as aquifer plugging, the Australian MAR Guidelines define clogging
as “the reduction in permeability of a porous medium” (NWQMS 2009, pg114). Clogging
leads to a reduction in flow rates, which limits the volume of water stored in the aquifer, or an
increase in head to maintain a constant recharge rate. It is important to understand the types
and causes of various forms of clogging and the associated management options, to define
source water treatments needs and maximize the operational life of the injection system.
1.3.2 Types and Causes
Clogging occurs due to reactions between the source water, target water and the aquifer
matrix as result of physical, chemical or biological mechanisms. Pyne (2005) identified that
following processes could be responsible for clogging:
Air entrapment;
Deposition of total suspended solids (TSS);
Biological growth;
Geochemical reactions;
Particle rearrangement in the aquifer materials.
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Each process is discussed in detail below:
Air entrapment
Air entrapment or gas binding is caused by the cascading of water inside the injection well
casing or air entering the recharge pipe network under negative pressure, producing air
bubbles that may block pore spaces in the aquifer matrix and screened casing. It is similar to
bubble lock which can occur during bore/well development. The entrained air increases the
oxidation-reduction potential (ORP), which promotes microbial activity and geochemical
reactions, leading to further clogging. Air entrapment can also occur due to the release of
dissolved gases through temperature or pressure changes or as a metabolic byproduct of
microbial activity (release of nitrogen or methane). Pyne (2005) suggests that clogging by air
entrapment is characterised by a rapid increase in flow resistance as shown in Figure 31.
Deposition of total suspended solids (TSS)
The accumulation of organic and inorganic suspended solids, such as clay and silt particles,
algae cells or their tests (diatoms), microorganism cells, can form a low permeability
clogging layer on injection surfaces (Bouwer 2002). Dillon et al. (2001) asserts that the
deposition of suspended sediments is the most frequently reported form of clogging.
Biological growth
Microbial clogging occurs through the growth of microorganisms and the production of
biofilms (extracellular polysaccharides). Pyne (2005) states that clogging due to biological
growth is not well understood. Recharge waters rich in organic carbon, nitrogen and
phosphorus, promote biological clogging and it is a commonly reported issue in recharge
basins (NWQMS 2009). Schuh (1990) identified that biological clogging in surface
infiltration systems can vary seasonally, in response to changes in water temperature and
viscosity.
Geochemical reactions
Chemical clogging is the result mineral precipitation affecting aquifer permeability.
Common geochemical reactions are the precipitation of calcium carbonate (calcite), gypsum,
phosphate, iron and manganese oxide hydrates (Bouwer 2002; Pyne 2005). Bacteria catalyse
many geochemical reactions therefore it can be difficult to separate chemical clogging from
biological clogging. These reactions occur due to the changes in redox conditions inherent in
injection of oxygenated water into typically reduced aquifers.
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Particle rearrangement
Particle rearrangement in the aquifer matrix, caused by repeated cycles of recharge and
recovery, can affect aquifer permeability (Olsthoorn 1982). Australian MAR Guidelines
(NWQMS 2009) fail to identify the particle rearrangement as a clogging mechanism however
Pyne (2005) states that particle rearrangement is not an important mechanism in aquifer
clogging but still must be considered.
1.3.3 Management options
A well-designed and constructed system is critical to the effective operation of an MAR
scheme. Brown et al. (2000) suggested that air entrapment clogging issues could be
eliminated through the design phase in an ASR case study at Hamersley Iron‟s Nammuldi
iron ore mine in the Pilbara. Youngs et al. (2010) suggested that the rapidly constructed pipe
network, paired with poor scouring practices, promoted physical clogging of injection wells
at FMG‟s Cloudbreak operation.
Evidence of several options to manage injection well clogging exist in the literature:
Injection well redevelopment: The periodic redevelopment or backflushing of
injection wells by airlifting or pumping is the preferred method to manage clogging
according to Pyne (2005). The frequency of redevelopment depends on the rate of
clogging and can vary from daily to annually.
Pre-treatment of injection water: This is common for reinjection schemes where the
end use is for potable purposes or the quality of the source water is significantly lower
than the target aquifer. Bouwer (2002) indicates that in addition to reducing the
effects of clogging, pre-treatment of water enables the protection of the receiving
groundwater quality.
Alterations to MAR infrastructure: Youngs et al. (2010) remediated the physical
clogging of FMG‟s Cloudbreak operation by removing the slotted PVC casing, in
addition to well redevelopment.
Chemical treatment: Chlorine and chemical treatments such as mineral acids,
organic acids, biodispersants, surfactants and enzymes are utilised as a rehabilitation
procedure (Pyne 2005). It is effective against biological clogging but is limited in
mitigating physical clogging.
1.3.4 Diagnostic tools
E.19
Various techniques have been reported on in the literature to predict clogging potential. These
include:
Membrane Filtration Index (MFI);
Water quality parameters;
Laboratory column experiments;
Numerical modeling;
Graphical techniques
Membrane Filtration Index (MFI)
The MFI method provides a relatively easy field assessment of physical clogging potential
(Dillon et al. 2001). Membrane filtration tests are used to develop an MFI. The test involves
passing recharge water through a membrane of fixed aperture at a constant pressure whilst
measuring the decline in flow rate. An example of the apparatus used in the test is shown in
Figure 32. The MFI is then determined graphically from the slope of the linear portion of the
time/volume (t/V) vs. volume (V) plot. Dillon et al. (2001) states that the greater the slope,
the higher the MFI and the greater potential for physical clogging. As the standardised
membrane is unlikely to be representative of the pore spaces in the aquifer, the test provides a
guide only and cannot be relied upon as an absolute measure.
Water Quality Parameters
Water quality parameters can be useful indicators of clogging potential. Measurement of
turbidity and TSS indicate physical clogging while total organic carbon (TOC), dissolved
organic carbon (DOC) and assimilable organic carbon (AOC) indicate biological clogging
(NWQMS 2009).
Laboratory Column Experiments
Laboratory column studies, also known as the parallel filter index (PFI), are determined by
passing recharge water through columns filled with aquifer material (Bouwer 2002; Wood et
al. 2005; Rinck-Pfeiffer et al. 2000). Due to the small-scale nature of the PFI test, this method
is not usually representative of the field scale processes.
Modeling
Youngs et al. (2010) applied the PHREEQC geochemical model to determine the potential of
chemical clogging. The model relies on water chemistry and hydrogeological data to predict
the potential for mineral precipitation.
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Graphical techniques
Simple graphical techniques to predict clogging have been proposed by Pyne (2005), where
the relationship between resistance to flow and time is compared with standard curves for
each clogging type (Figure 31).
1.4 Application of MAR in the Mining Industry
1.4.1 Water Management in Mining
There is huge potential for MAR to be applied to the mining industry to aid mine site water
management, although only few studies investigate this prospect. The Australian MAR
Guidelines (NWQMS 2009) and investigations into legislation and policy governing MAR in
Australia (Ward and Dillon 2012) make no reference to the potential for MAR in a mining
context.
Dewatering of open pits and underground mining areas to enable safe conditions for below
water table (BWT) mining can result in large volumes of water abstracted from the orebody
aquifer. Current water management practices endeavor to use this supply for operational
requirements, which include dust suppression and ore processing. However, often the
dewatering abstraction volumes exceed mine site water demand. In such cases of water
surplus, excess water is discharged to the surface water environment and is effectively
wasted. Over the life of mine, water balances can fluctuate between water surplus and water
deficit depending on mine planning, pit sequencing of BWT deposits and climatic conditions.
MAR has the potential to buffer these fluctuations by banking water during periods of water
surplus to meet future water demand in a deficit scenario.
In addition to managing fluctuations in the minesite water balance, MAR has several benefits
over water management strategies currently applied in the industry. Firstly, the disposal of
excess dewatering volumes into ephemeral surface water system leads to negative ecological
and cultural implications. Youngs et al. (2010) state that the constant discharge provides a
water source for ecosystems, which then become dependent on minesite operations and
acknowledged that surface discharge is discouraged by traditional landowners. Secondly, the
reinjection of the dewatering surplus reduces the net groundwater drawdown of the minesite
operations. MAR can also be used to mitigate impacts to groundwater dependant ecosystems
(GDE) proximal to mine dewatering by reducing drawdown at GDEs.
E.21
Compared to the conventional MAR projects intended to secure potable supply, MAR
schemes in an operational mining environment are designed „fit for purpose‟. This means that
mining-related MAR schemes are typically designed and constructed rapidly on a larger
scale, operated over a shorter life-of-project duration with less emphasis on control of water
quality (given the primary objective of water disposal).
1.4.2 MAR Schemes in the Pilbara Region
The potential for ASR as a minesite water management tool in the Pilbara mining region of
Western Australia has had limited attention in the scientific literature. Only few authors have
investigated this prospect (Windsor et al. 2011; Youngs et al. 2010; Brown et al. 2000;
Kneeshaw and Clark 1983). By examining two ASR case studies, Brown et al. (2009)
contended that in addition to meeting future water demand, MAR could reduce impact to the
surrounding surface water environment.
Reductions in impacts to GDEs are another potential benefit. The successful implementation
of a relatively large and complex MAR scheme at Fortescue Metals Group (FMG)‟s
Cloudbreak operation proves that MAR is a viable option for water managers in the mining
industry and is an „ideal‟ tool for the Pilbara region (Youngs et al. 2010). The scheme
consists of a saline and fresh water injection system and was implemented to bank fresh
water for future use and to maintain ecological water requirements of the nearby Fortescue
Marsh (Youngs et al. 2010).
1.4.3 Mining Specific Operational Considerations
Brown et al. (2000) identified three issues specific to the application of MAR in a mining
context:
4. The potential for injected water to re-circulate back into the dewatered orebody
aquifer, with implications for the efficiency of dewatering operations. The optimal
distance between dewatering operations and injection wells is dictated by the degree
of hydraulic connectivity between the orebody aquifer and the target injection aquifer.
Numerical modeling can be applied to predict the volumes of reticulated water
(Youngs et al. 2010).
5. The practical requirement of periodic well redevelopment due to a reduction in
hydraulic performance from the effects of injection well and aquifer clogging
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(discussed further in Section 2.2 Clogging) and the implications for the active mining
operations.
6. The impact of injecting poor quality water from the active mining area into a high
quality natural groundwater environment.
It should be noted that (3.) is not an issues solely related to MAR in a mining environment.
Studies concerning the injection of treated wastewater to secure drinking water supply
(Bosher et al. 1998; Asano and Levine 1998; Pavelic and Dillon 1997) have also highlighted
the issue of managing the interaction of low quality source water with a higher quality target
aquifer. An issue not identified by Brown et al. (2002) is the effect of reinjection on nearby
mining operations downstream of the MAR site and the implications on their water balance
and dewatering requirements.
1.5 Conclusion
Recent case studies assessed as part of this literature review demonstrate that MAR is viable
option in mine site water management. However, the feasibility of any project is largely site
specific and relies on an understanding the local and regional hydrogeology. A sound
understanding of the physical, chemical and microbial processes occurring within the aquifer
during natural and under MAR is important to manage the ASR system with confidence
(Dillon et al. 1999). Looking forward, the abundance of future below water table (BWT)
deposits across the Pilbara region is likely to increase the potential of MAR to sustainably
manage mine site water resources. Currently, there no guidelines exist to advise on the
optimal MAR system characterisation and design in Pilbara-specific mining environment.
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2 Project Proposal
2.1 Title
“Clogging mechanisms in Managed Aquifer Recharge: a case study at Mining Area C”.
2.2 Investigator
Investigator:
Lily Smith (21224043)
Project Supervisors:
Coordinating Supervisor Associate Professor Ryan Vogwill, School of Earth and
Environment, UWA
Co-supervisor Jed Youngs, BHP Billiton Iron Ore
2.3 Introductory Statement
The proposed study will discuss the operation of Managed Aquifer Recharge in a mining
setting while investigating the clogging potential and its mitigation in injection wells at
Mining Area C (MAC).
2.4 Objective
The objectives of the proposed study are:
To describe the MAR system characterisation and design at MAC;
To assess the operational performance of the MAR system;
To determine the potential for various types of clogging in the injection wells at
MAC;
To develop a Pilbara-specific tool for identifying, assessing and managing clogging
(time permitting).
2.5 Background
Managed Aquifer Recharge (MAR) is the process of artificially recharging the groundwater
for storage and subsequent recovery or environmental benefit. Clogging of the injection well
and aquifer interface is a commonly reported operation issue for such schemes and can occur
E.24
via physical, biological, chemical or mechanical means (Pavelic and Dillon 1997; Baveye et
al. 1998).
MAC is a BHP Billiton owned and operated open pit iron ore mine, situated 100km northeast
of Newman in the Pilbara region of WA. The site is currently experiencing a surplus water
balance and has identified MAR as a sustainable and effective way of disposing excess
abstraction volumes from in-pit dewatering. The testing and commissioning phase has been
underway since March 2012 and a 2-year trial is scheduled to commence in November 2012.
The ASR system at MAC disposes of excess dewatering supply via a temporary storage dam
which is piped to three injection bores as shown in Figure 33. The injection bores are located
adjacent to the A Deposit orebody, downstream of current mining activities, and will be
utilised as abstraction bores for dewatering purposes for future mining at this deposit. The
injection bores are screened in the regional Wittenoon dolomite fractured rock aquifer.
Preliminary results from the testing commissioning phase indicate that clogging is a potential
issue at one of the three injection wells.
2.6 Significance
In Australia and worldwide, MAR has been successfully applied to a number of projects
involving wastewater recycling and stormwater harvesting. However, there is huge potential
for MAR to be applied to the mining industry to aid mine site water management. The MAR
trial at MAC is the first of such schemes to be implemented by BHP Billiton Iron Ore. As
below water table (BWT) mining increasingly becomes a necessary element of future mine
sites in the iron ore project pipeline, practical knowledge of effective design, operation and
management of the MAR system is key for successful project implementation.
2.7 Methodology
The project will be investigated according to the steps outlined below:
1. Describe the MAR system characterisation:
Site Description;
Aquifer Characterisation;
o Describe local and regional geology;
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o Create a conceptual cross section diagram of the study area showing the major
aquifer units, direction of groundwater flow, monitoring/injection bores with
screens depths;
o Determine aquifer parameters via test pumping (February 2013);
o Tracer test, to determine the degree of connection between aquifer units.
Groundwater Characterisation;
o Assess the baseline groundwater quality through analysis of hydrochemistry
sampling.
System Design Characterisation;
o Describe the MAR system design, from source water to injection;
o Describe the construction of injection wells.
2. Determine operational performance:
Aquifer response to MAR:
o Analyse mounding (m) vs. time (days), (Appendix 2).
o Analyse mounding (m) vs. discharge Rate (kL/day)
o Test these responses with numerical modeling studies using MODFLOW.
Note that the model will be developed by an external consultant and only
utilised for scenario analysis as part of this project.
3. Identify and quantify ASR well clogging potential and type:
Clogging diagnostic tools:
o Analyse water quality parameters during the injection trial, with an emphasis
on TSS;
o Compare mounding (m) vs. time (days) to standard curves, to determine type
of clogging mechanism occurring;
o Investigate and apply a PHREEQC geochemical model, to simulate chemical
reactions and transport processes occurring in the injection well and surround
aquifer.
Downhole camera study, followed by scrape samples and analysis on casing.
If time permitting:
4. Develop a diagnostic tool for identifying, assessing and managing clogging:
Test robustness of diagnostic tool on data from BHP Billiton‟s Olympic Dam MAR
scheme (if available).
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2.8 Budget
Refer to Appendix 3.
2.9 Timetable
Refer to Appendix 3.
E.27
3 References
ANZECC. (2000). Australian and New Zealand Guidelines for Fresh and Marine Water
Quality, Volume 1. Australian and New Zealand Environment and Conservation Council.
AquaGeo. (2013). BHPBIO Pump Test Analysis; Mining Area C. Perth, Western Australia:
Report for BHP Billiton.
Asano, T., & Levine, A. (1998). Wastewater reclamation, recycling and reuse: an
indroduction. In T. Asano, Wastewater reclaimation and reuse. Lancaster, Pennsylvania:
Technomic.
ASCE. (2001). Standard Guidelines for Artificial Recharge of Grounwater. Standard
ASCE/EWRI 34-01. United States: American Society of Civil Engineers.
Barber, C. (2010). Evaluation of potential risks to groundwater quality from reinjection of
mine dewatering fluids: Mining Area C, Pilbara. Perth, Western Australia: Internal Report.
Crisalis International Pty Ltd.
Baveye, P. V., Hoyle, B. L., Deleo, P. C., & De Lozada, D. S. (1998). Environmental Impact
and Mechanisms of the Biological Clogging of Saturated Soils and Aquifer Materials.
Critical Reviews in Environmental Science and Technology , 28 (2), 123-191.
BHP Billiton. (2011). Managed Aquifer Recharge Trial: Works Approval Application
Supporting Document.
Boehmer, K. W. (1998). Re-Assessment of Sustainable Abstraction from GroundwaterBasins
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E.33
Figures
Figure 28 Types of MAR schemes (Dillon, 2005)
Figure 29 Map of Managed Aquifer Recharge in Australia as of 2011 (Ward and Dillon, 2012)
E.34
Figure 30 Global application of artificial recharge (IGRAC, 2012)
Figure 31 A graphical diagnostic tool for determining different mechanisms of clogging (Pyne, 2005)
E.35
Figure 32 Apparatus for a membrane filter test used to determine MFI (Dillon et al., 2001)
E.36
Figure 33 MAR Monitoring Network at MAC
E-37
Figure 34 Units of the Hamersley Region (Kneeshaw 2008).
E-38
Tables Table 9 Examples of MAR systems adapted from NWQMS (2009)
MAR System
Type
Description Locality of example
Aquifer Storage and Recovery (ASR)
Injection into a well for storage and recovery from the same well to a confined or unconfined aquifer.
Grange, and Tea Tree Gulley,Adelaide, South Australia
Aquifer Storage, Recovery and Transport (ASTR)
Injection into a well for storage and recovery from a different well for water quality treatment purposes.
Salisbury, South Australia
Vadose Zone Wells
Injection into a dry well to allow infiltration to a deep unconfined aquifer.
Phoenix, United States
Percolation tanks and recharge weirs
Construction of a dam or weir in an ephemeral stream channel to allow infiltration to unconfined aquifers and subsequent recover downstream.
Callide Valley, Queensland
Rainwater harvesting
Diversion of roof runoff into a well or sump filled with sand or gravel.
Perth, Western Australia
Bank filtration Extraction from a well near or under a surface water body to induce infiltration.
Berlin, Germany
Infiltration galleries
Infiltration through geotechnically-stabilised buried trenches to an unconfined aquifer.
Floreat Park, Western Australia
Dune filtration Construction of a pond in a dune to allow infiltration for extraction at lower elevations.
Amsterdam, The Netherlands
Infiltration ponds Construction of a pond or channel off-stream to allow infiltration to an underlying unconfined aquifer;
Burdekin Delta, Queensland
Soil aquifer treatment
Diversion of treated sewage effluent to infiltration ponds.
Alice Springs, Northern Territory
Underground dams
Construction of a trench across an ephemeral streambed, backfilled with low permeability material for flood management purposes.
Northeast Brazil
Sand dams Construction of a sand dam on an ephemeral stream to create an artificial aquifer following periods of inundation.
Kitui, Kenya
Recharge releases Construction of a dam on an ephemeral stream, followed by the slow release of water to promote downstream infiltration.
Little Para River, South Australia
E-39
Table 10 Summary Table of the MAR Monitoring Bore Network H
ole
Nam
e
Eas
ting
(MG
A94
_50)
Nor
thin
g
(MG
A94
_50)
DE
C R
eq’t
Sc
reen
ed
Aqu
ifer
Uni
t
Geo
logi
cal
Des
crip
tion
SWL
@ ti
me
of
drill
ing
(mbg
l) Sc
reen
D
epth
(mbg
l)
Log
ger
Typ
e
Inte
rval
(hrs
)
HGA0001P* 709317.122 7463243.316 Y Paraburdoo DOLOMITE 45.27 82-115 VWP 4
HGA0002P* 709561.560 7463202.707 Y Paraburdoo DOLOMITE 42.63 77.5-125.5 VWP 4
HGA0003P* 709769.705 7463142.631 Y
Paraburdoo DOLOMITE: Blue-green-grey karstic dolomite, yellow and pale grey
shales @ 78 – 80m
42.66 48-106 VWP 4
HGA0035M 709306.264 7463243.275 N TD? N/A N/A 6-65 700 1
HGA0036M 709576.278 7463209.241 N
TD? N/A N/A 6-65 700 1
Paraburdoo? DOLOMITE?
HGA0006M 709778.852 7463152.497 N
Paraburdoo DOLOMITE: Blue-green-grey karstic dolomite, yellow and pale grey
shales @ 78 – 80m
42.49 72-91 300 1
HGA0015M 708935.000 7463355.500 Y
Paraburdoo DOLOMITE: Grey, green grey, yellowgrey, minor fracturing
throughout, somelimonite staining
52.0 106.8-142.8 300 3
HGA0010M 709472.830 7463024.000
Y
Paraburdoo DOLOMITE: Grey, light grey N/A 136.3-176.3 300 3
West Angela GOETHITE: Dark brown (yellow streak) with abundant dark grey,
blackmanganese in bands and invading gothite
CLAY: Brown with minor BIF
BIF: Dark grey, dark red becoming grey, brown, yellow, little
mineralisation 162-165m partly mineralised
HGA0009M 709481.490 7462932.010 Y TD TD2 50.00 33.4-63.4 300 3
E-40
HGA0008M 709482.650 7462976.460 Y TD TD2 49.00 31.7-61.7 300 3
HGA0013M 709228.310 7463148.910
Y
West Angelas SHALE AND BIF: Multicoloured (yellow,brown, white, pink, red,
grey), soft and hard clays, grits and small rock fragments - mainly
bif, shale and chert
N/A 117-132 300 3
Mount
Newman
BIF AND CHERT: Yellow-brown, caramel and red, siliceous BIF
and CHERT
HGA0012M 709229.170 7463009.470 Y TD TD1 55.00 52-82 300 3
HGA0011M 709240.790 7463073.020 Y
TD TD1/2 N/A 66-96 300 3
West Angelas
HGA0007M 709729.310 7462883.980
Y
Paraburdoo DOLOMITE: Grey, mainly fresh, minor fractures with limonite,
minor brown, yellow brown bands
N/A 114-150 300 3
West Angelas BIF: Dark grey, dark red becoming dark grey, yellow brown, hard,
siliceous
GWB0023M 709306.920 7463357.140 N
TD TD2 N/A N/A 700 3
Paraburdoo DOLOMITE
GWB0025M 709547.510 7463441.080 N Paraburdoo DOLOMITE N/A N/A 700 3
HGA0024M1 708396.204 7463325.575 N
Marra Mamba GOETHITE and minor limonite: Dark grey-yellow, numerous
fractures, hard texture, moderately weathered, minor chert
60.08 108-120 300 3
HGA0017M 708411.000 7463380.000 N
Mount
Newman
GOETHITE: ore, dark grey, fine grained, dull lustre N/A 123.9 –
147.9
300 3
HGA0025M1 708394.712 7463511.336
N
Paraburdoo DOLOMITE and minor shale: Dark red-yellow-grey, dolomite (40-
60%), shale (5-40%), weathered BIF (10-45%), hard texture,
fractures
57.50 108-120 300 3
HGA0022M 707314.000 7463574.660 N
Mount
Newman
BIF: Multi coloured chips in green, yellow green matrix, banded,
grey, green grey, yellow brown
64.65 104.7-116.7 300 3
E.41
Appendix 1
Referencing Style
E.42
E.43
E.44
Appendix 2
MAC MAR Trial - Monitoring Bore Network
Figure 33 shows the spatial distribution the monitoring bore network used to assess the aquifer
response to the MAR trial. The details of each bore are summarised in Table 10.
The ‘Requirement’ column refers to bores required to be monitored under the Environmental
Protection Act 1986, Licence L7851/2002/4. Over the 2 yr trial period, the conditions of the
licence state that water levels in the injection bores (HGA0001P, HGA0002P and HGA0003P)
must be recorded daily for the first two weeks, then weekly for the first six months, then monthly
subject to review and monitoring bores recorded weekly. The logger monitoring network
frequency described below exceeds these legal requirements outlined by the DEC.
‘Geological Descriptions’ entries are missing due to bores not being logged or a function of poor
record keeping.
„Logger Type’:
700: In-Situ Level Troll 700 Vented logger, records pressure (kPa), temperature (°C) and depth (m);
300:In-Situ Level Troll 300Non-vented logger, records pressure (kPa), temperature (°C) and depth (m);
VWP: Vibrating Wire Piezometer, records frequency (Hz) and temperature (°C).
The ‘Screened Aquifer Units’ refer to the geological units of the Hamersley Region, as described
by Kneeshaw (2008) in Figure 34.
E.45
Appendix 3
Project proposal Budget and Timetable
Budget
A nominal budget of $7, 200 is proposed.
ITEM COST
Laboratory analysis of water quality samples $3,000
Tracer study $2,000
Camera study $2,000
Printing and binding $100
Miscellaneous $100
TOTAL $7, 200
BHP Billiton Iron Ore can contribute to the cost where work is relevant to mine site
operations.
E.46
Timetable
2012 2013 2014 Task J J A S O N D J F M A M J J A S O N D J F M A M Preliminary research, define research proposal
Proposal seminar Research and draft literature review
Research proposal draft due Review research proposal with supervisors
Research proposal final due
Field work
Analyse monitoring data
Draft thesis
Thesis draft due Review thesis draft with supervisors
Thesis final due
Milestones
Proposal Seminar 11/09/2012
Research Proposal Draft Due 09/10/12
Research Proposal Final Due 06/11/12
Thesis Draft Due 04/04/14
Thesis Final Due 30/05/14