Streamline-Based Production Data Integration in Naturally Fractured Reservoirs
- Mishal H. Al-Harbi (Texas A&M U.) | Hao Cheng (Texas A&M U.) | Zhong He (Texas A&M U.) | Akhil Datta-Gupta (Texas A&M U.)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2005
- Document Type
- Journal Paper
- 426 - 439
- 2005. Society of Petroleum Engineers
- 5.8.7 Carbonate Reservoir, 4.1.2 Separation and Treating, 5.1 Reservoir Characterisation, 5.1.8 Seismic Modelling, 5.6.4 Drillstem/Well Testing, 5.3.1 Flow in Porous Media, 5.5.7 Streamline Simulation, 5.8.6 Naturally Fractured Reservoir, 5.6.5 Tracers, 5.5.8 History Matching, 4.3.4 Scale, 7.6.2 Data Integration, 5.5 Reservoir Simulation, 4.1.5 Processing Equipment, 5.1.5 Geologic Modeling
- 2 in the last 30 days
- 640 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Streamline-based models have shown great potential in reconcilinghigh-resolution geologic models to production data. In this paper, we extendthe streamline-based production-data integration technique to naturallyfractured reservoirs. Describing fluid transport in fractured reservoirs posesadditional challenges arising from the matrix/fracture interactions. We use adual-porosity streamline model for fracture-flow simulation by treating thefracture and matrix as separate continua that are connected through a transferfunction. Next, we analytically compute the sensitivities that define therelationship between the reservoir properties and the production response infractured reservoirs. The sensitivities are an integral part of our approachand can be evaluated very efficiently as 1D integrals along streamlines.Finally, the production-data integration is carried out by a generalizedtravel-time inversion that has been shown to be robust because of itsquasilinear properties and that uses established techniques from geophysicalinverse theory.
We also apply the streamline-derived sensitivities in conjunction with adual-porosity finite-difference simulator to combine the efficiency of thestreamline approach with the versatility of the finite-difference approach.This significantly broadens the applicability of the streamline-based approachin terms of incorporating compressibility effects and complex physics. Wedemonstrate the power and utility of our approach using 2D and 3D syntheticexamples designed after actual field conditions. The reference fracturepatterns are generated using a discrete fracture network (DFN) model thatallows us to include statistical properties of fracture swarms, fracturedensities, and network geometries. The DFN is then converted to a continuummodel with equivalent gridblock permeabilities. Starting with prior models withvarying degrees of fracture information, we match the water-cut history fromthe reference model. Both dual-porosity streamline and finite-differencesimulators are used to model fluid flow in the fractured media. Our resultsindicate the effectiveness of our approach and the role of prior informationand production data in reproducing fracture connectivities and preferentialflow paths.
|File Size||2 MB||Number of Pages||14|
1. Nelson, R.A.: Geological Analysis of Naturally Fractured Reservoirs, GulfPublishing Co., Houston (1985).
2. Dershowitz, B. et al.: "Integration of Discrete FractureNetwork Methods With Conventional Simulator Approaches," SPEREE (April2000) 165.
3. Tamagawa, T. et al.: "Construction of Fracture NetworkModel Using Static and Dynamic Data," paper SPE 77741 presented at the 2002SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 29September-2 October.
4. Long, J.C.S. et al.: "Component Characterization: An Approach to FractureHydrogeology," Subsurface Flow and Transport: The Stochastic Approach, G. Daganand S.P. Neuman (eds.), Cambridge U. Press (1997) 179-195.
5. Datta-Gupta, A. et al.: "Detailed Characterization of aFractured Limestone Formation by Use of Stochastic Inverse Approaches,"SPEFE (September 1995) 133.
6. Vasco, D.W., Yoon, S., and Datta-Gupta, A.: "Integrating Dynamic Data IntoHigh-Resolution Reservoir Models Using Streamline-Based Analytic SensitivityCoefficients," SPEJ (December 1999) 389.
7. He, Z., Yoon, S., and Datta-Gupta, A.: "Streamline-Based Production DataIntegration With Gravity and Changing Field Conditions," SPEJ (December2002) 423.
8. Qassab, H. et al.: "Streamline-Based Production DataIntegration Under Realistic Field Conditions: Experience in a GiantMiddle-Eastern Reservoir," paper SPE 84079 presented at the 2003 SPE AnnualTechnical Conference and Exhibition, Denver, 5-8 October.
9. Wang, Y. and Kovscek, A.R.: "A Streamline Approach toHistory-Matching Production Data," paper SPE 59370 presented at the 2000SPE/DOE Symposium on Improved Oil Recovery, Tulsa, 3-5 April.
10. Milliken, W.J., Emanuel, A.S., and Chakravarty, A.: "Application of 3-D StreamlineSimulation to Assist History Matching," paper SPE 63155 presented at the2000 SPE Annual Technical Conference and Exhibition, Dallas, 1-4 October.
11. Barenblatt, G.E., Zheltov, I.P., and Kochina, I.N.: "Basic Concepts inthe Theory of Seepage of Homogenous Liquids in Fissured Rocks," J. Appl. Math.and Mech. Eng. Transl. (1960) 1286.
12. Kazemi, H. et al.: "Numerical Simulation of Water-Oil Flowin Naturally Fractured Reservoirs," SPEJ (December 1976) 317; Trans., AIME,261.
13. Dean, R.H. and Lo, L.L.: "Simulations of Naturally FracturedReservoirs," SPERE (May 1988) 638; Trans., AIME, 285.
14. Di Donato, G., Huang, W., and Blunt, M.J.: "Streamline-Based Dual PorositySimulation of Fractured Reservoirs," paper SPE 84036 presented at the 2003SPE Annual Technical Conference and Exhibition, Denver, 5-8 October.
15. Al-Huthali, A. and Datta-Gupta, A.: "Streamline Simulation ofCounter-Current Imbibition in Naturally Fractured Reservoirs," Journal ofPetroleum Science and Engineering (2004) 43, 271.
16. Cheng, H. et al.: "FastHistory Matching of Finite-Difference Models Using Streamline-DerivedSensitivities," SPEREE (October 2005) 426.
17. Wu, Z. and Datta-Gupta, A.: "Rapid History Matching Using aGeneralized Travel-Time Inversion Method," SPEJ (June 2002) 113.
18. Cheng, H. et al.: "FieldExperiences With Assisted and Automatic History Matching Using StreamlineModels," paper SPE 89857 presented at the 2004 SPE Annual TechnicalConference and Exhibition, Houston, 26-29 September.
19. Guerreiro, L. et al.: "Integrated ReservoirCharacterization of a Fractured Carbonate Reservoir," paper SPE 58995presented at the 2000 SPE International Petroleum Conference and Exhibition inMexico, Villahermosa, Mexico, 1-3 February.
20. King, M.J. and Datta-Gupta, A.: "Streamline Simulation: A CurrentPerspective," In Situ (1998) 22, No. 1, 91.
21. Datta-Gupta, A. and King, M.J: "A Semianalytic Approachto Tracer Flow Modeling in Heterogeneous Permeable Media," Advances inWater Resources (1995) 18, 9.
22. Pollock, D.W.: "Semianalytical Computation of Pathlines forFinite-Difference Models," Groundwater (1988) 26, No. 6, 743.
23. Kazemi, H. et al.: "Analytical and Numerical Solution ofOil Recovery From Fractured Reservoirs With Empirical Transfer Functions,"SPERE (May 1992) 219.
24. Bratvedt, F., Gimse, T. and Tegnander, C.: "Streamline Computations for PorousMedia Flow Including Gravity," Transport in Porous Media (1996) 25, 63.
25. Bissel, R.C., Killough, J.E., and Sharma, Y.: "Reservoir History Matching Using theMethod of Gradients on a Workstation ," paper SPE 24265 presented at the1992 SPE European Petroleum Computer Conference, Stavanger, 24-27 May.
26. Landa, J.L. and Horne, R.N.: "A Procedure to Integrate Well TestData, Reservoir Performance History, and 4D Seismic Information Into aReservoir Description," paper SPE 38653 presented at the 1997 SPE AnnualTechnical Conference and Exhibition, San Antonio, Texas, 5-8 October.
27. Wen, X.-H., Deutsch, C.V., and Cullick., A.S.: "High-Resolution Reservoir ModelsIntegrating Multiple-Well Production Data ," SPEJ (December 1998) 344.
28. Reynolds, A.C., He, N., and Oliver, D.S.: "Reducing Uncertainty inGeostatistical Description With Well Testing Pressure Data," Proc., 1997 Intel.Reservoir Characterization Conference, Houston, 2-4 March.
29. Anterion, F., Eymard, R., and Karcher, B.: "Use of Parameter Gradients forReservoir History Matching," paper SPE 18433 presented at the 1989 SPESymposium on Reservoir Simulation, Houston, 6-8 February.
30. Vega, L., Rojas, D., and Datta-Gupta, A.: "Scalability of the Deterministic andBayesian Approaches to Production Data Integration Into Field-Scale ReservoirModels," SPEJ(September 2004) 330.
31. Paige, C.C. and Saunders, M.A.: "LSQR: An Algorithm for Sparse LinearEquations and Sparse Least Squares," ACM Trans. Math. Software (1982) 8, No. 1,43.
32. Vega, L.: "An Efficient Bayesian Formulation for Production DataIntegration into Reservoir Models," PhD dissertation, Texas A&M U., CollegeStation, Texas (2003).
33. Schlumberger GeoQuest: ECLIPSE User Guide 2003A.
34. Liu, N and Oliver, D.S.: "Evaluation of Monte Carlo Methodsfor Assessing Uncertainty," SPEJ (June 2003) 188.