history match

8/10/2019 History Match

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ITB - Reservoir Simulation Course, Bandung

Reservoir Simulation

History Match


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2 ITB - Reservoir Simulation Course, Bandung Frebruary, 2009

Reservoir Simulation

Geological model

Reservoir model

Field development

History Match

Forecast

Performance of a reservoir simulation study


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Data review

Reservoir Simulation Study


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Observed flow rates are imposed on wells during the history period

One expect to reproduce:

pressure evolution

WCT and GOR gas or water breakthrough

production rates

Inconvenients:

Many data are unknown (no information available far from wells)

It is not obvious to detect the most influent data (all data act together)

Some artefacts must be corrected

Main Issues


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Methodology overviewWorkflow

INITIAL MODEL

SIMULATION RUN

FORECAST RUN

MODIFICATION OF

GEOMODEL

MODIFICATION OF

PARAMETERS

GOOD

MATCH

NEW GEOMODEL

YES

YES

NO

NO


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Remind

Geologist and geophysics must work hard to help the reservoirengineer to maintain the consistency of the geological model

It is better to have rough, consistent matching than matching

which is accurate but destroys the model

Methodology overview


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Methodology overview

General well information:All the wells penetrating the reservoirs

with their associated general parameters: XY coordinates, KB,

and surveys

Well markers

Structural depths maps:3D seismic interpretation loaded with the

associated fault network.

Interpreted well logs:from the petrophysical evaluation (Volume

of Shale, Effective Porosity, Water Saturation, Lithology).

Rock types

Petrophysical properties: Net-to-gross, porosity, permeabilities

Rock types:kr-Pc, water saturation, volumes in place

Production data:static pressure, flowing pressure, production

rates, WCT, GOR, WBT, GBT.

Available data


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Like all physical assets, data require maintenance over time. Raw data will

degrade when errors are introducedtypically through human

intervention, as when data are manually entered into spreadsheets or

various processing routines used for decision making.

Data errors are easily generated; a misplaced decimal, typographical error

or erroneous map datum can relegate well data to a new geographical

province, redraw the boundary of a field, change the structure of a

productive horizon or alter a completion strategy.

The information technology industry has devised a systematic

methodology to address oilfield data quality and validation issues.

Data analysis: QualityMethodology overview


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The DQM methodology relies on six basic criteria, or measurement

categories, to evaluate data quality:

Validity: do the data make sense, honour science and corporate standards? Completeness: does the client have all of the required data?

Uniqueness: are there duplicate items in the same data store?

Consistency: do the attributes of each item agree between data sources?

Audit: has an item been modified, added or deleted?

Data changes: have any attributes of an item been modified?

These measurement categories translate into business rules for

assessing the data.

Data Quality ManagementMethodology overview


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Key features

Field basis match:

Faults

Aquifer

Global permeability scaling

Vertical Transmissivities

Well by well match:

Local Transmissivities X, Y, Z

Relative permeabilities endpoint scaling (Swi, Sor) Local PI and skin


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Key features

Late well behaviour correspond to area far from the wells

Do not limit your analysis close to the wells to match late production

time reservoir parameters

Early well behaviour correspond to area close to the wells:

Concentrate on well data to match early production times local

parameters

Flow directions are not correct if pressure is not matched:

Do not try to match in saturation if you are not matched in pressure

Modification of matching parameters:

Try to anticipate model reactions by using simple calculations Do not introduce new parameters without a look back to geologists &

geophysicians.


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Key featuresField basis match

Global patterns of:

Production rates

Water cut

Cumulate production

Reservoir pressure


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Key featuresWell by well match

Oil production rate and cumulated oil

production

Water cut


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Key featuresWell by well match

Time = 0 Time = 4000 days

Time = 8000 days Time = 12,000 days

0 0.25 0.5 0.75 1.0

Kro


Time = 8000 days Time = 12,000 days

0 0.225 0.45 0.675 0.9

Krw


Time = 8000 daysTime = 12,000 days

0 0.2 0.4 0.6 0.8 1.0

So

Relative permeability Relative permeability Oil saturation

of water of oil


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Matching parameters

Pressure match:

Volumes originally in place, Pc

Aquifer dimensioning

Faults modelling

Pore and fluid compressibility

Flow rates match:

Relative permeabilities

Transmissivities

Skin

PI

WCT and GOR:

Relative permeabilities

Transmissivities

Water and gas breakthrough:

Relative permeabilitiesend points

Transmissivities


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Pressure match

Objective: Get a correct evolution with time of the average reservoir pressure.

Pressure match is an adjustment of the reservoir energy balance between:

Volumes originally in place

Aquifer activity Pore and fluid compressibility

The material balance should address the whole reservoir voidage (no material

balance per fluid at surface conditions). The total fluid withdrawal at reservoir

conditions (reservoir voidage) is:

BwQwBgQoRsBgQgBoQoQres

Material balance


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Pressure matchMaterial balance

The data origin is mainly from build-up tests and or from RFT surveys run in

new wells.

Reservoir pressure deducted from DST need to be compared with an

average pressure calculated from the well surrounding cells and the well

block.

It is usual to calculate an average pressure from 5 grid cells (areal model)

weighed by the respective pore volumes (BP5 in summary section).


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Pressure match

Objective: Get a correct evolution of reservoir pressure versus time and

space.

Diffusivity equation:

Main parameters:

hydraulic diffusivity,K/(f..c)

permeability,K

fluid viscosity,

porosity, f

total compressibility, c

t

P

c

K

z

zgP

y

P

x

P

f

2

2

2

2

2

2 )(

Diffusivity equation


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Pressure match

Objective: Get a correct relationship between flow rate, reservoir pressure

and bottom hole flowing pressure.

Main parameters: Numerical productivity index or connection factor (CF):

Drainage area properties: Transmissivity distribution

Transfer functions: relative permeability and capillary pressure

Well's representation

Srr

hKCF

wo

well

)/ln(

2


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Pressure match

Objective: Get a correct relationship between flow rate, reservoir pressure

and bottom hole flowing pressure.



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Pressure match

Comparison between well cell pressure and BHP:


PRESSURE & FLOW RATE HISTORY INSTANTANEOUS PRESSURE PROFILE


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Pressure matchAquifer activity

A preliminary study with a MB software is necessary to run the aquifer

match. The expected result is the aquifer volume plus its permeability.

The aquifer volume is to be reproduced in the reservoir model with aquifer

cells or analytical functions.

Aquifer activity needs to be adjusted in order to reproduce the field observed

reservoir pressure history.

The reservoir model production history is run with all the producing wells

governed by the "reservoir voidage" option.


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Pressure matchFault modelling

Fault modelling: Only faults with influence in the zone of interest

are modeled.

definition

transmissivity


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Flow rates match

During the pressure match procedure, production rates are not

honoured.

Simulation is now run by setting the oil rate for producers so bottom hole

flowing pressure, gas and water rates are calculated by the simulator.

The phase rate matching consists of adjusting the calculated GOR and

WCT to the field measured values.

To honour the relationships between reservoir pressure, BHFP andphase rates, PI need to be adjusted. This is accomplished by applying

multiplication factors to the well perforation connection values: MULTPI.


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Flow rates match

Main parameters:

PermeabilitiesTransmissivities.

Permeability barriers (i.e. faults)

Relative permeabilities: shape and endpoints.


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Flow rates matchRelative phase permeabilities

Water cut (WCT): wateroil ratio

Water breakthrough (WBT): water production startsassociated

to Swi

Early water breakthrough

impacted by rock-type effectskr curves


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Flow rates matchRelative phase permeabilities


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Objectives

Modify relative permeabilty tables in an easy way, kr tables are normalized and

remain always the same, only the end-points are changed and kr curves are then

recalculated.It's a useful option in History Match simulations.

Flow rates matchEndscale option


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KRO: Maximum oil relative

permeability

KRORW: Oil relative permeability at

critical water saturation Swcr

KRWR: Water relative permeabilityat residual oil saturation (1-Sowcr)

KRW: Maximum water relative

permeability

SWL: Connate water saturation

SWCR: Critical water saturation

SOWCR: Residual oil saturation

SWU: Maximum water saturation

Flow rates matchkeywords


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If the 3-points scaling is to be used, add in the PROPS section:

SCALECRS

YES /

Flow rates match


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If the 2-points scaling is used, relative permeabilities are calculated as follows:

If the 3-points scaling is used, relative permeabilities are calculated as follows:

Flow rates match


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0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

1,00

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1Sw

Kr

krw un-scaled

kro un-scaled

Krw 2-point scaling

krw 3-point scaling

kro 2-point scaling

kro 3-point scaling

SWL= 0.24

SWCR= 0.35

Flow rates matchExample 1


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SWL= 0.20

SWCR= 0.25

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

1,00

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Sw

Kr

krw un-scaled

kro un-scaled

Krw 2-point scalingkrw 3-point scaling

kro 2-point scaling

kro 3-point scaling

Flow rates matchExample 2


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Flow rates matchWater breakthrough

Matching breakthrough times is a difficult task.

Breakthrough times are sensitive to truncation errors (numerical dispersion)

and the accurate matching requires finer grid than normally necessary.

Using a LGR is a possibility to the use of pseudo-relative permeabilities can

help.

An unsuccessful attempt for a match indicates that some of the basic

assumptions of the model (geology, structure, volumes, extensions, PVT

behaviour, energy balance between initial hydrocarbon in place and aquiferactivity) may have to be revised.


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Cumulated Water (bubble diagram from OFM)


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The gridding techniques include local gridding (LGRs) for the creation of small cells

around wells for improved resolution, useful to match the water breakthrough and

water cut when conning effects are present.

LGR


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Uncertainty in predictions

Uncertainty contributions


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Uncertainty in predictions

Uncertainty contributions

Take a look at the figure below looking at the range of possible production from the same

development plan but using differently history matched models.

The range of possible outcomes is wide.

Are you drilling in areas that have a much higher risk than is apparent today?

Or are you perhaps missing out on developments that have potential?


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History matchImportance


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In history Matching, observed average rates are known; controls

are simple.

VFP tables are introduced at the end of history matching process

to ensure the continuity between matching runs (set measured Q)

and prediction runs (limit THP).

Well controls: history match

Main Controls


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History Match

ECLIPSE keywords


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Eclipse keywords

Well definition & controls: SCHEDULE Section

SCHEDULE

--restart results

RPTRST

--well specification and completion

WELSPECS

COMPDAT

--production constraints in history match

WCONHIST

--timestep management and tolerance criteriaTUNING

DATES

1 'AUG' 2008 /

/

END


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Eclipse keywords

Well definitions

WELSPECS

-- 1 2 3 4 5 6--name group i j BHP_ref_dep phase

P1 'PROD' 20 7 2500 'OIL' /

/

Well P1 belongs to group PROD

Well head is at i=20, j=7

BHP reference depth of 2500. Defaults to depth of top-most connection

OIL is the preferred phase (used only for PI output)

Other items can usually be defaulted

WELSPECS: General specification data for wells


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Eclipse keywords

Well completions

COMPDAT

-- 1 2 3 4 5 6 7,8 9 10 11--name i j k1 k2 status diameter skin

P1 20 7 3 8 'OPEN' 2* 0.15 1* 2 /

/

Well P1 is completed in layers 3 to 8 of colum i=20, j=7

The well bore diameter is 0.15 m and the skin is +2

Eclipse will compute the connection factor using the Peaceman formula:

for a vertical well

using kh values of the completed cells

COMPDAT: Well completion specification data


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Eclipse keywords

Well completionsCOMPDAT

-- 1 2 3 4 5 6 7 8 9 10 11 12 13

--name i j k1 k2 status CF diam kh skin direction

P1 20 7 3 3 'OPEN' 1* 23.47 0.15 /P1 20 7 4 4 'OPEN' 1* 6.14 0.15 /

P1 20 6 4 4 'OPEN' 1* 8.25 0.15 /

P1 20 6 5 5 'OPEN' 1* 94.70 0.15 520.3 2 1* Z /

/

P1 is a deviated well crossing columns (20,7) and (20,6) completed in layers3 to 5

The CF have been calculated in SCHEDULE application and input in item 8

the well bore diameter must be given

kh, skin and direction of penetration may be given for information as in the last

line above


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Eclipse keywords

Modify Connection Factors

WPIMULT--name factor

P1 2.0 /

'P2' 0.5 4 25 6 /

/

Multiplies all the connection factors of well P1 by 2.0 Multiplies the connection factor of the completion of well P2 in cell (4,25,6)

by 0.5

WPIMULT: Multiplies well connection factors by a given value within

local grids


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Eclipse keywords

Well control

WCONHIST:

Specific to production wells in history matching

Sets the observed rates, per phase, in surface conditions

Calculates the production rate depending on the chosen control mode

WHISTCTL:

Allows to change only the control mode; for example, to pass from a

reservoir rate control to a surface oil rate control

WCONINJH: for injection wells

This keywords can be created with SCHEDULE application.


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Eclipse keywords

Well control

WCONHIST

-- 1 2 3 4 5 6 7 8 9 10

--name status control Qos Qws Qgs VFP Qgl THPobs BHPobs

P1 'OPEN' 'RESV' 255 15 1000 0 1* 1* 150 /

/items:

2: choice between 'OPEN' (default), 'SHUT' & 'STOP' (allows cross flow)

3: choice between 'ORAT' 'WRAT' 'GRAT' 'LRAT' 'RESV'

4,5,6: observed surface rates used in the calculation of the constraint with respect to the

control mode stated in 3 and/or to be compared to simulated rates (i.e. WWCT versus

WWCTH)7: VFP table number used in the calculation of tubing head pressure, otherwise 0

9: observed value of THP copied in the file .UNSMRY (WTHPH) to be compared to the

calculated value

10: observes value of pressure (flowing, static, build-up...) copied in the file .UNSMRY

(WBHPH) to be compared to the calculated value.


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Eclipse keywords

Well control: remarks

Avoid using 'OPEN' with nil rates when wells are shut

'RESV' control is recommended for pressure matching

the equivalent reservoir rate is calculated from the surface flow rates at the

average pressure of the region stated in WELSPECS

Bottom hole pressure limit

default = 1 bar

it may be changed using WELTARG after the first WCONHIST for the well

In item 10, a reservoir pressure can be given (static, build-up) to be

compared to the calculated pressure WBP or WBP9 SUMMARY section

WOPRH, WWCTH, WBHPH.... keywords represent the observed values to be

compared to the calculated values WOPR, WWCT, WBHP....

Not a default output

BgQosRsBgQgsBoQosBwQwsQ fondT ,


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Eclipse keywordsWell control

WHISTCTL

-- 1 2--new status BHP action

ORAT 'NO' /

item

1: choice between 'ORAT 'WRAT' 'GRAT' 'LRAT' 'RESV' 'NONE'

2: action if the bottom hole limit pressure is reached:'YES' : run stop

'NO' : wells controlled by bottom hole pressure (default)

WHISTCTL: Influences the control of all HM wells. During history

matching, it allows to override the control mode set in subsequent

WCONHIST keywords


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Eclipse keywords

Well control for injectors

WCONINJH

-- 1 2 3 4 5 6 7 8

--name phase status Qinj BHPobs THPobs VFP Rs/Rv

I1 'WATER' 'OPEN' 1000 1* 300 2 1* /

/

items:

2: choice between 'WATER' 'GAS' 'OIL'

3: choice between 'OPEN' (default), 'SHUT', 'STOP' (allows cross flow)

4: observed injection rate

5,6: observed BHP and THP

7: VFP table number

8: gas concentration in the injected oil or condensate concentration in the injected gas


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For production wells: BHP, VFP table or gas lift quantity:

For injection wells: injection rate value or BHP:

Eclipse keywordsWell control (optional)

WELTARGP1 'BHP' 100 /

WELTARG: Resets a target or limit value defined in WCONHIST or

WCONINJH

WELTARG

I1 'WRAT' 1000 /

WSALT, WTRACER: Define salt or tracers concentration for injection

wells)


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P


Exercise

History Match


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Introduction to Rhombo case Geometry

Top reservoir at 1960 m TVDSS

Reservoir thickness of 50 m

Petrophysics

Porosity and permeability derived from cores

KH derived from well test

Fluid properties

PVT properties derived from fluid analysis

Initial state

Initial pressure = 250 bars at 2000 m TVDSS

Water-Oil contact assumed at 2160 m TVDSS

Saturation functions

Relative permeability and capillary derived from SCAL analysis

Aquifer activity

Unknown

Production data

Well P3 put into production during 4 years


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Rhombo caseTop of the reservoir


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Rhombo case: Geometry x-z cross section

P3


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Rhombo case: Reservoir layering

Layer Net thickness

(m)

Net porosity

(%)

Net permeability

(mD)

Phi x H

(m)

KH

(mD.m)

1 6,6 19,9 63,4 1,31 418

2 5,9 17,5 3,2 1,03 19

3 7,8 20,1 92,7 1,57 728

4 8,6 20,7 200,8 1,78 1687

5 8,8 21,5 473,0 1,89 4176

Well 37,7 20,1 62,1 7,59 7028


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Rhombo case: Fluid properties

Oil properties

Stock tank oil density = 849,7 kg/m3

Gas solution factor = 124,1 m3/m3@ Psat

Saturation pressure = 220 bara

Oil volume factor = 1,153 vol/vol @ Psat

Compressibility = 0,5 x 10-4bar-1(under saturated)

Viscosity = 1,20 cP @ Psat

Gas properties

Stock tank oil density = 0,9 kg/m3

Gas volume factor = 0,0059 rm3/m3@ 220 bara

Viscosity = 0,026 cP @ 220 bara

Water properties Water density = 1000,5 kg/m3

Formation volume factor = 1,01 vol/vol @ 250 bara

Compressibility = 0,44 x 10-4bar-1

Viscosity = 0,481 cP


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Rhombo case: oil PVT functions


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Rhombo case: gas PVT functions


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Rhombo case: W/O SCAL


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Rhombo case: G/O SCAL


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Rhombo case: Aquifer simulation


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Rhombo case: Aquifer simulation

Internal radius: ri= 3710 m

Aperture: q= 15,4

Compressibility: Caquifer = Cr + Cw = 10-4bar-1

Petrophysics:

Layer Net thickness

(m)

Net porosity

(%)

Net permeability

(mD)

Phi x H

(m)

KH

(mD.m)

1 6,6 19,9 21,1 1,31 139

2 5,9 17,5 1,1 1,03 6

3 7,8 20,1 31,1 1,57 243

4 8,6 20,7 65,4 1,78 562

5 8,8 21,5 158,2 1,89 1392

Total 37,7 20,1 62,1 7,59 2343


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Rhombo case: issues to investigate

Fluids

Calculate oil and gas compressibility in reservoir conditions

Saturation functions

Calculate w/o & g/o mobility ratio in reservoir conditions

Initial state

Calculate wateroil transition heightNatural depletion

Calculate the contribution of rock compaction & fluid expansion to

reservoir voidage

Look at ECLIPSE results

Calculate the OOIP, oil recovery, oil production, GOR, WCT vs timewith no aquifer, infinite aquifer, numerical aquifer.


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Rhombo case: History Match

History match will be attempted on the Rhombo case

Production data to match are:

Oil production

Cumulative oil production

Water production Water breakthrough time & water cut rise after WBT

Gas production

Gas breakthrough time & GOR rise after GBT

Reservoir pressure

Average reservoir pressure & bottom hole flowing pressure


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Possible matching parameters:

Aquifer volume

Permeability in the lowest layer

Kv/Kh anisotropy ratio Maximum water relative permeability


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Rhombo case: Matching parameters

Aquifer volume Adjusted with a PV multiplier in the outer cells


Adjusted with a TX multiplier

Kv/Kh anisotropy ratio

Adjusted with PERMZ/PERMX ratio

Maximum water relative permeability

Adjusted with relative permeability curves


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Uncertainty ranges for matching parameters are: Aquifer volume

Use a PV multiplier between 1 and 100 in the outer cells


Use a TX multiplier between 0,2 and 2,0

Kv/Kh anisotropy ratio Use a Kv/Kh anisotropy ratio between 0,1 and 0,01


Use a krw max between 0,2 and 0,4


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Run 0 will correspond to the following data: Aquifer volume

PV multiplier set to 50 in the outer cells


TX multiplier set to 1,0

Kv/Kh anisotropy ratio Kv/Kh anisotropy ratio set to 0,05


krw max set to 0,3


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Rhombo case: Production data P3

Liquid rate(m3/d)

WCT(%)

GOR(m3/m3)

WBHP(bar)

Cumulative oil(Mm3)

FPR (bar)

01/01/03 750 0,0 124 230,0 0,001 253,4

01/07/03 750 0,0 125 193,1 0,114 233,1

01/01/04 750 0,0 127 187,1 0,274 226,0

01/07/04 750 0,0 131 182,6 0,388 223,2

01/01/05 750 0,0 139 177,7 0,542 219,4

01/07/05 750 0,0 150 171,2 0,648 216,5

01/01/06 750 1,3 170 154,5 0,794 212,3

01/07/06 750 10,1 188 120,6 0,895 208,9

01/01/07 750 19,9 214 96,7 1,007 204,1


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Simulate run 0 and perform the following sensitivity tests: Aquifer volume

Use a PV multiplier between 1 and 100 in the outer cells


Use a TX multiplier between 0,2 and 2,0

Kv/Kh anisotropy ratio Use a Kv/Kh anisotropy ratio between 0,1 and 0,01


Use a krw max between 0,2 and 0,4

For each simulation

Identify main production mechanisms during production history

Look at the main parameters linked to these mechanisms

Draw some conclusions

Work to do... first part


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Identify the two most influent parameters

By looking at the sensitivity runs

By relating these parameters to production mechanisms

Give new ranges for these two parameters

To take into account the results of this first screening

To prepare a second screening

Work to do... first part


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Rhombo case: Sensitivity runs

AquiferMULTPV

Layer 5MULTX Kv/Kh krw max

Cum.Oil

Mm3

FinalLrate

m3/d

FinalBHP

bar

WBTyears

FinalWCT

%

FinalGOR

m3/m3

0 run 0 50 1,0 0,05 0,30

1 low Aq 1 1,0 0,05 0,30

2 high Aq 100 1,0 0,05 0,30

3 low TX 50 0,2 0,05 0,30

4 high TX 50 2,0 0,05 0,30

5 low kv/kh 50 1,0 0,01 0,30

6 high kv/kh 50 1,0 0,10 0,30

7 low krw 50 1,0 0,05 0,20

8 high krw 50 1,0 0,05 0,40

MATCH ? ? ? ?


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Use first screening simulations

To define a new run 0 and basic sensitivity tests

Update the ECLIPSE data file

Simulate the new run 0 and launch new sensitivity tests tocomplete the second screening

Try to explore as much as possible all the possible cases

Try to anticipate model reactions before launching a newsimulation

Give values of the 4 parameters corresponding to your best match

Work to do... second part

history match

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