Post on 03-Apr-2015
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