A database approach for constraining object- and pixel-based stochastic simulations of fluvial sedimentary architecture:

example application to quantification of connectivity

Luca Colombera, Nigel P. Mountney, William D. McCaffrey, Fabrizio Felletti

Fluvial & Eolian Research Group – University of Leeds

OverviewCreation of a relational database for the digitization of fluvial sedimentary architecture :

the Fluvial Architecture Knowledge Transfer System (FAKTS)

Quantitative characterization of fluvial architecture applicable to:

• determination of importance of controlling factors

• develop quantitative synthetic depositional models

• derive constraints on subsurface predictions

• identify modern and ancient reservoir analogues

Approach to

DB designThe sedimentary and geomorphic architecture of preserved ancient successions and modern rivers is translated into the database schema by subdividing it into geological objects – common to the stratigraphic and geomorphic realms – which belong to different scales of observation nested in a hierarchical fashion.

FAKTSFAKTS conceptual and logical schemesafter Colombera et al. (2012)

Implementation

2 classes:Channel-complex

Floodplain

GENETIC UNITS CLASSIFICATIONSGENETIC UNITS CLASSIFICATIONSDEPOSITIONAL ELEMENTS

ARCHITECTURAL ELEMENTS

FACIES UNITS

14 classes of subenvironments:Genetic bodies/facies associations

with geomorphic significance

25 textural ± structural classeslargely based on

Miall’s (1996) scheme

DATASET/SUBSET CLASSIFICATIONSDATASET/SUBSET CLASSIFICATIONSMETADATA

INTERNAL PARAMETERS

EXTERNAL CONTROLS

• Authors/reference• Basin• Lithostratigraphic unit• River• Age• Methods/data type• Data Quality Index• etc…

• Basin gradient• Discharge regime• River pattern• Drainage pattern• Aggradation rates• Load-type dominance• Relative distality• etc…

• Subsidence rates/types• Basin/catchment climate• Basin/catchment vegetation• Relative eustatic change• Catchment lithologies• Catchment uplift rates• Catchment geomorphic processes• etc…

Data Entry

North (1996): “at present, much is being published in the format of multiple vertical profiles, photomontages and line drawings because we still do not really know how to handle all the available facts.”

Cain (2009)Cain (2009)

Cain (2009)Cain (2009)

Amorosi et al. (2008)Amorosi et al. (2008)

Robinson & Robinson & McCabe(1997)McCabe(1997)

Database Output UNIT PROPORTIONS

North (1996): “at present, much is being published in the format of multiple vertical profiles, photomontages and line drawings because we still do not really know how to handle all the available facts.”

Database Output UNIT DIMENSIONS

Miall & Jones (2003): “the database on large-scale fluvial architecture, especially sandbody width and length, remains extremely small”

Database Output UNIT TRANSITIONS

N = 1024

Facies transition within 4Facies transition within 4thth order channel-fills order channel-fills

Transition count matricesCOUNT (Z) Sh Sl Sm Sp Sr Ss St …

Sh 555 116 218 145 211 59 169 …Sl 122 283 151 89 25 33 121 …

Sm 215 142 561 119 51 25 103 …Sp 143 87 106 350 56 22 155 …Sr 152 19 50 37 121 4 76 …Ss 68 55 16 20 7 58 57 …St 208 145 124 137 103 42 698 …… … … … … … … … …

Reservoir/aquifer analogue selection

FAKTS contains now:

4,285 classified large-scale depositional elements, 3,446 classified architectural elements, 20,101 facies units;

from 111 case studies, including :

25 modern rivers,65 ancient successions,2 other published composite databases.

UP-TO-DATE FIGURESUP-TO-DATE FIGURES

Synthetic analogues

North & Prosser (1993): “Are the results from outcrop and modern environment studies being translated into predictive tools suitable for modelling subsurface geology?”

Subsurface applications

de Marsily et al. (2005): “future work should be focused on improving the facies models […] A world-wide catalog of facies geometry and properties, which could combine site genesis and description with methods used to assess the system, would be of great value for practical applications.”

QUANTITATIVE INFORMATION FROM:

• identified modern and ancient reservoir analogues

• synthetic depositional models used as synthetic analogues

TO BE USED FOR:

• guiding subsurface correlations

• deriving static-connectivity models

• obtaining constraints to stochastic facies modelling:genetic/material unit: proportions, absolute and relative dimensional parameters, Indicator auto- and cross-variograms, transition probabilities/rates…

FAKTS facies-modelling applications

after Guo & Deutsch (2010)

after Mariethoz et al. (2009)

after Deutsch & Tran (2002)

MODEL-CONDITIONING PROBLEMSMODEL-CONDITIONING PROBLEMS

FAKTS provides a wealth of quantitative data – from several classified case studies – with which to fully constrain stochastic structure-imitating simulations of fluvial reservoir/aquifer architecture, overcoming the main problems encountered when relying on traditional databases.

OVERVIEWOVERVIEWFAKTS facies-modelling applications

FAKTS facies-modeling applicationsGeometrical parameters

OBJECT-BASED SIMULATION CONSTRAINTSOBJECT-BASED SIMULATION CONSTRAINTS

after Colombera et al. (In press)

FLUVSIM (Deutsch & Tran 2002) simulations

Relative dimensional parametersOBJECT-BASED SIMULATION CONSTRAINTSOBJECT-BASED SIMULATION CONSTRAINTS

FLUVSIM (Deutsch & Tran 2002) simulations

after Colombera et al. (In press)

CHCHFFFF

CSCS

Relative dimensional parameters can be derived as FAKTS stores genetic-unit absolute sizes, transitions and hierarchical nesting.

FAKTS facies-modelling applications

Material unitsPIXEL-BASED SIMULATION CONSTRAINTSPIXEL-BASED SIMULATION CONSTRAINTS

Material units defined on any categorical and/or continuous variable: flexibility in the choice of reservoir-quality categories.

after Colombera et al. (In press)

FAKTS facies-modelling applications

Indicator auto-variogramsPIXEL-BASED SIMULATION CONSTRAINTSPIXEL-BASED SIMULATION CONSTRAINTS

It is possible to inform indicator auto-variogram model form and parameters on material-unit proportions and modality, mean and variance in size, for each FAKTS direction.

FAKTS facies-modelling applications

Indicator cross-variogramsPIXEL-BASED SIMULATION CONSTRAINTSPIXEL-BASED SIMULATION CONSTRAINTS

after Colombera et al. (In press)

Indicator cross-variograms can be informed on FAKTS-derived:-Proportions p-Transition rates r (from transition frequency and mean size)

FAKTS facies-modelling applications

Transition probabilities/rates and lithotype rulesPIXEL-BASED SIMULATION CONSTRAINTSPIXEL-BASED SIMULATION CONSTRAINTS

Possibility to derive parameters that enable the simulation of genetic- and material-unit spatial relationships and juxtapositionaltrends.

FAKTS facies-modelling applications

INCLUDING BOUNDING-SURFACE INFORMATIONINCLUDING BOUNDING-SURFACE INFORMATIONFAKTS facies-modelling applications

Static-connectivity studiesMULTI-SCALE CONNECTIVITY ANALYSIS OF CLASSIFIED FLUVIAL SYSTEMSMULTI-SCALE CONNECTIVITY ANALYSIS OF CLASSIFIED FLUVIAL SYSTEMS

Connectivity functionDownstream direction

Possibility to investigate the impact of several scales of heterogeneity on reservoir static connectivity and its variability associated with types of fluvial depositional systems.

Static-connectivity studiesMULTI-SCALE CONNECTIVITY ANALYSIS OF CLASSIFIED FLUVIAL SYSTEMSMULTI-SCALE CONNECTIVITY ANALYSIS OF CLASSIFIED FLUVIAL SYSTEMSPossibility to investigate the impact of several scales of heterogeneity on reservoir static connectivity and its variability associated with types of fluvial depositional systems.

Future workInclusion of porosity and permeability data for every order of genetic units.

After Anderson et al. (1999)

Dynamic-connectivity studies for assessing architectural controls

and N:G threshold between connectivity-limited and

permeability-heterogeneity-limited reservoirs for a range of

different classified fluvial systems.

ConclusionsFAKTS major advantages for conditioning facies modelling:

•possibility to choose different modelling categories corresponding to different scales of heterogeneity, and adopt a multi-scale approach;•possibility to define any type of material units (on any categorical and/or continuous variable) to be used as modelling categories;•possibility to derive absolute and relative dimensional parameters with which to condition object-based simulations; •possibility to generate models of indicator auto- and cross-variograms with which to constrain variogram-based simulations;•possibility to obtain transition frequency/probability matrices with which to constrain Markov chain-based simulations or with which to establish lithotype rules or contact matrices for plurigaussian simulations;•possibility to employ database output to fully constrain unconditional simulations of fluvial architecture and to use the resulting realizations as 3D training images for multiple-point-statistics simulations.

Thank you for you attention

IAS is thanked for travel grant

We thank our sponsors