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Project Work JIP Forward

Project Work JIP Forward. Rahul Saraf. Objectives. Continued Development and Demonstration of Workflow for Well Path location optimization Study Traditional Optimization based on Individual Geomodel vs Ensemble based Optimization

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Project Work JIP Forward

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  1. Project Work JIP Forward Rahul Saraf

  2. Objectives • Continued Development and Demonstration of Workflow for Well Path location optimization • Study Traditional Optimization based on Individual Geomodelvs Ensemble based Optimization • Demonstration of the use of Hierarchal Optimization with Genetic Algorithm(GA)

  3. Well path location Optimization • Finding a well location which produces maximum oil and thus makes more money • Two ways : -kh(Effective Permeability)/kh*Saturation approach : (Well Path Screening Task :MEPO) *Kh(Init /Grid File Reader) *Saturation (Restart File Reader) -FOPT(Oil Production) (Eclipse Simulation) • One Well!!!-ES/CMAES/GA More than one Well-Hierarchal(ES/GA)

  4. Reservoir Description:BAYER GAS FIELD • Gas condensate reservoir • GWC > 3000 m • Faults • Two main formations • Initial Gas In place > 5 BSm³ • Initial Reservoir Pressure ~ 500 bar • Simulation model dimesions: ~ 45 x 55 x 180

  5. X Y Z Search Space 1 Reference Layer Min. x1, Min y1 Max. x1 Search Space 2 Max. y1 Min. x2, Min y2 Max. x2 Dz Max. y2 Dz Well Path

  6. Polar Coordinates : Radius ,Angle,dz for tip and toe(5 UP per well) plus Depth through python script And WellPath to File Rectangular Co ordinates (x,y,k,dz) for tip and toe .So(6 UP per well).No python script!!!

  7. In case of Well Path Screening W1, W2,W3 are Kh*Sat for respective Wells and Field

  8. Workflow : Well Path Screening Task

  9. Workflow : FOPT Optimization

  10. ENSEMBLE BASED OPTIMIZATION vs TRADITIONAL INDIVIDUAL GEOMODEL APPROACH

  11. Background • A geomodel is based on core data collected at various locations on the field. • Properties of each grid cell is estimated by spatial distribution of these core data • Uncertainities and Large number of possible realization

  12. MODEL G1-Optimistic-P90 G2-Most Probable-P50 G3-Pessimistic-P10

  13. 0.3 0.5 0.2 Or G123 G123 0.3*G1+0.5*G2+0.2*G3=G123

  14. Traditional Approach G1-Optimistic-P90 P1 G2-Most Probable-P50 P2 G3-Pessimistic-P10 P3

  15. Workflow : Traditional Approach

  16. Decision Well Location : Traditional Approach 0.3*G1(P1)+0.5*G2(P1)+0.2*G3(P1)=G123(P1) 0.3*G1(P2)+0.5*G2(P2)+0.2*G3(P2)=G123(P2) 0.3*G1(P3)+0.5*G2(P3)+0.2*G3(P3)=G123(P3) Final Well Location or Position and Oil Production based on best of G123(P1), G123(P2) or G123(P3) So it is either P1/P2/P3

  17. Ensemble Approach P Or G123 G123 Optimization is carried out directly on the weighted/risked model as a result the position for maximum oil production would be different.Also it is expected that the maximum oil produced is better than all the three results obtained from traditional approach.

  18. Workflow : Ensemble Approach

  19. Present Status • Due to time constraint to get some intermediate result were obtained by LatinHypercube Experimental Design instead of Optimization. • GA with Hierarchial Optimizer case still in Development will be launched very soon!!!

  20. EXTRA INNINGS

  21. Optimization Concept

  22. Hierarchical Optimization Workflow SPE 38014 The Stratigraphic Method: A structured approach to History Matching M.A. Williams et al.

  23. Application Objectives: • Find the optimum well position and design by maximising the cumulative gas production • Well candidates must be evaluated on the basis of three risked geological realisations • Compare results of optimising the well path on individual geo realisations with the ‘multi-model’ concept

  24. Assumptions for Multi-objective Criteria • The global objective depends on a number of partial objectives • Assumption: no or little correlation among objectives with and • The mutual optimum is found by optimizing partial objectives

  25. Optimizer Relationship

  26. Example: Optimize Global = F1 + F2

  27. Example: Optimizer F1, F2 independently

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