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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. Outline. Motivation : Getting reliable production forecasts

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  1. Joint Structural and Petrophysical History Matchingof Stochastic Reservoir ModelsThomas SCHAAF* & Bertrand COUREAUDScaling up and modeling for transport and flow in porous media ConferenceDubrovnik, 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

  3. Motivation Decision taking in uncertain environment  Getting reliable production forecasts

  4. CPU intensive, non linear Numerical Modeling Steps Uncertain Input Parameters Outputs of interest Decision Making Objective Function Data Assimilation Under-determinded Problem Current Methodology 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

  5. 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))

  6. CONDOR (IFP R&D version) GEOMODELER Generic component : launch any exe file in the workflow Geomodeler workflow manager Proposed workflow (1/2) • 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

  7. 1 1 2 3 2 3 Proposed workflow (2/2) 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

  8. Synthetic 3D waterflooding model Geological Model : 5038100 Simulation Model : 201620 • 3 zones : • Top : Sequential Gaussian Simulation for poro/perm • Middle : Object based stochastic modeling • Bottom : SGS for poro/perm

  9. Inversion Parameters set Fault throw Fault transmissivity Channels orientation Channels proportion kvkh ratio Mean k value for SGS Geological Model : 5038100 + Sorw = 7 parameters

  10. Synthetic 3D waterflooding model Final oil saturation field Observation Data • 2 oil producers, 1 injector : 12 years of production history • Observation data : Fine scale fluid flow simulation results BHP & WCT

  11. CONDOR GEOMODELER Condor inversion parameters have their counterpart in the geomodeler internal workflow Condor inversion parameters (Initial value, lower & upper bounds) History Matching Process • 7 parameters : Channels (%,dir), Fault (throw,T),kvkh, Sorw, Mean_kx

  12. GEOMODELER WORFLOW MODELED GEOLOGICAL MODEL $throw = 15 m $Chan_dir = 90° History Matching Process • Concrete view of the Geomodeler workflow runs :

  13. GEOMODELER WORFLOW MODELED GEOLOGICAL MODEL $throw = 25 m $Chan_dir = 110° Grid modified @ each iteration ! History Matching Process • Concrete view of the Geomodeler workflow runs :

  14. Fault Throw Management • Freeze NW seismic horizons • Apply the throw to SE horizons

  15. Initial OF value History Matching Results • Gradients based constrained optimization (not optimal, P. King work) • Numerical gradients computation (no adjoints …)

  16. «Optimal» OF value History Matching Results • Gradients based constrained optimization • Numerical gradients computation

  17. History Matching Results Summary

  18. 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!

  19. Joint Structural and Petrophysical History Matchingof Stochastic Reservoir ModelsThomas SCHAAF* & Bertrand COUREAUDScaling up and modeling for transport and flow in porous media ConferenceDubrovnik, 13-16 October 2008

  20. Back up

  21. Gradual Deformation Method

  22. 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

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