Joint Structural and Petrophysical

History Matching

of Stochastic Reservoir Models

Thomas SCHAAF* & Bertrand COUREAUD

Scaling up and modeling for transport and flow in porous media Conference

Dubrovnik, 13-16 October 2008

Dubrovnik, 13-16 october 2008 2

Outline

Motivation : Getting reliable production forecasts

Current methodology

Focus on the History Matching process

Proposed workflow to perform joint HM

Test case : Synthetic 3D waterflooding model

History Matching process & results

Conclusions & Perspectives

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Decision taking in uncertain environment

Getting reliable production forecasts

Motivation

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Current Methodology

UncertainInput

Parameters

NumericalModeling

Steps

Outputs ofinterest

DecisionMaking

DataAssimilation

CPU intensive, non linear

Under-determindedProblem

ObjectiveFunction

3 steps approach: Sensitivity study with respect to the OF (ED+proxy model) Multiple History Matching processes with remaining parameters Propagation of uncertainties to forecasts using those HM models

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History Matching Process

Updating simultaneously geological and simulation models

But structural and petrophysical uncertainties are seldom tackle at the same time; leading to sub optimal History Matched models

All the ingredients are currently available to go ahead

(Rivenæs & al.(2005) ; Suzuki & Caers(2008))

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Assisted History Matching (AHM) softwares are mature & versatile Geomodeling softwares have powerful internal workflow managers Geomodeling softwares can be launch in batch mode

Capitalize on existing geomodeling projects Consider both structural and petrophysical HM

Proposed workflow (1/2)

Geomodeler workflow managerGeneric component : launch any exe file in the workflow

CONDOR (IFP R&D version)GEOMODELER

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From a practical point of view :

Condor writes a text file with current inversion parameters value Condor launches the geomodeler that :

reads that file assigns the values to its own internal variables launchs its internal workflow :

Structural modeling, Facies modeling, poro/perm modeling, Upscaling, export of the data file

Condor launches the fluid flow simulator Condor get the simulation results, computes the OF value Parameters updating Next iteration

Capitalize on existing projects Consider both structural and petrophysical HM

Proposed workflow (2/2)

1

1

2

2

3

3

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Synthetic 3D waterflooding model

Geological Model : 5038100 Simulation Model : 201620

3 zones : Top : Sequential Gaussian Simulation for poro/perm Middle : Object based stochastic modeling Bottom : SGS for poro/perm

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Inversion Parameters set

Geological Model : 5038100

Channels orientationChannels proportion

Fault throwFault transmissivity

kvkh ratio

Mean k value for SGS

+ Sorw = 7 parameters

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Synthetic 3D waterflooding model

Final oil saturation field

2 oil producers, 1 injector : 12 years of production history Observation data : Fine scale fluid flow simulation results BHP & WCT

Observation Data

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7 parameters : Channels (%,dir), Fault (throw,T),kvkh, Sorw, Mean_kx

History Matching Process

CONDOR GEOMODELER

Condor inversion parameters(Initial value, lower & upper bounds)

Condor inversion parametershave their counterpart

in the geomodeler internal workflow

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Concrete view of the Geomodeler workflow runs :

History Matching Process

GEOMODELER WORFLOW MODELED GEOLOGICAL MODEL

$throw = 15 m$Chan_dir = 90°

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Concrete view of the Geomodeler workflow runs :

History Matching Process

GEOMODELER WORFLOW MODELED GEOLOGICAL MODEL

$throw = 25 m$Chan_dir = 110°

Grid modified @ each iteration !

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Freeze NW seismic horizons Apply the throw to SE horizons

Fault Throw Management

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Gradients based constrained optimization (not optimal, P. King work) Numerical gradients computation (no adjoints …)

History Matching Results

Initial OF value

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Gradients based constrained optimization Numerical gradients computation

History Matching Results

«Optimal» OF value

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History Matching Results Summary

Initial value+ bounds

Optimal value(coarse scale

simul)

Reference case(fine scale simul)

Chan_frac(%) 20 [15;35] 34.16 30

Tfault 0.05 [0.01;0.5] 0.0138 0.2

Sorw 0.3 [0.15;0.35] 0.229 0.25

throw(m) 30 [10;40] 18 15

Mean_kx(mD) 50 [40;200] 177.28 120

Kvkh 0.005[0.001;0.05]

0.001 0.01

Chan_dir(°) 110 [80;120] 99.31 90

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Conclusions & perspectives

Full History Matching Process : technicaly & operationnaly ok

Lead to more robust integrated geological stochastic reservoir models

More reliable production forecasts Ongoing work :

Better integration of the HM process in the global Geophysics / Geology /

Reservoir Engineering Process eg. (fault throw / velocity model updates)Geologicaly realist updating of the reservoir structure !

Parameterization/updating of the geological scale fields (facies,poro,

perm) eg. gradual deformation, geomorphing techniques. Prior sensitivity study should be done Test gradients free algorithms : GA, simplex, PSO, VFSA, NEWUOA, hybrid

or even better, Bayesian Approach!

Joint Structural and Petrophysical

History Matching

of Stochastic Reservoir Models

Thomas SCHAAF* & Bertrand COUREAUD

Scaling up and modeling for transport and flow in porous media Conference

Dubrovnik, 13-16 October 2008

Back up

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sin( )cos( )

Gradual Deformation Method

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Outline

Motivation : Getting reliable production forecasts Current methodology:

Sensitivity study Multiple History Matching (HM) processes Propagation of uncertainties to forecasts

Focus on the History Matching process : Updating both geological and simulation models Necessity to tackle both types of uncertainty : structural and petrophysical

Proposed workflow : Versatile assisted HM softwares Geomodeling software internal workflow manager

Test case : Synthetic 3D waterflooding model History Matching process & results Conclusions & Perspectives