Application of Artificial Intelligence to Characterize Naturally Fractured Reservoirs

Proceedings - SPE International Improved Oil Recovery Conference in Asia Pacific

Volumn , Issue , 2003, Pages 135-148

  El Ouahed, A Kouider ^a   Tiab, Djebbar ^b   Mazouzi, Amine ^a   Jokhio, Sarfraz A ^b  

^a SONATRACH   (Algeria)

^b Oklahoma University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY INFERENCE SYSTEMS (FIS); NATURALLY FRACTURED RESERVOIRS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; COMPUTER SIMULATION; FRACTURING (OIL WELLS); FUZZY SETS; GEOCHEMISTRY; MECHANICAL PERMEABILITY; POROSITY;

PETROLEUM RESERVOIR ENGINEERING;

EID: 1842486633     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Haykin, S.: "Neural Networks," McMaster University Hamilton, Ontario, Canada. Prentice Hall International, Inc.
2. Hagan, M.T.: Howard B. Demuth, Mark Beal,: "Neural Network Design", PWS Publishing Company.
3. Demuth, H. and Beale, M: "Neural Network Toolbox for use with MATLAB", User's Guide, Version 4, MATLAB 6.0.
4. Demuth, H. and Beale, M.: "Fuzzy Logic Toolbox for use with MATLAB", User's Guide, Version 2, MATLAB 6.0.
5. Tiab, D.: "Advances in Petrophysics, Volume 1- Flow Units." Norman, Oklahoma, U.S.A. 2000.
6. Richradson, Q.S. and Weiss, W.W.: "Fractured Reservoir Characterization and Performance Forecasting Using Geomechanics and Artificial Intelligence," paper SPE 30572
7. Zellou, A.M., Ouenes, A. and Banik, A.K: "Improved Fractured Reservoir Characterization Using Neural Networks, Geomechanics and 3-D Seismic," paper SPE 30722
8. Ouenes, A., Zellou, A.M., Nova, T., Basinski, P.M. and Head, C.F.: "Practical Use of Neural Networks in Tight Gas Fractured Reservoirs: Application to the San Juan Basin." Paper SPE 39965
9. Ouenes, A. and Hartley, L.: "Integrated Fractured Reservoir Modeling Using Both Discrete and Continuum Approaches." Paper SPE 62939
10. Ouenes, A.: "Practical application of Fuzzy Logic and Neural Networks to Fractured Reservoir Characterization", Computers and Geosiences, Vol.26, pp. 953-962, 2000.
11. Barman, A., Ouenes, A. and Wang, M.: "Fractured Reservoir Characterization Using Streamline-Based Inverse Modeling and Artificial Intelligence Tools." Paper SPE 63067.
12. Sadiq, T. and Nashawi, I.S.: "Using Neural Networks for Prediction of Formation Fracture Gradient." Paper SPE 65463.
13. Eduardo, Quintero, J., Martinez, L.P. and Gupta, A: "Characterization of Naturally Fractured Reservoirs Using Artificial Intelligence." paper SPE 67286.
14. Balch, R., Weiss, W. and Wo, S.: "Correlation of Traditional Wire-Line Logs to FMI Estimated Secondary Porosity", For: Yates Petroleum Corporation -April 1998.
15. Soto, R. B., Garcia, J.C., Torres, F. and Perez, G.S.: "Permeability Prediction Using Hydraulic Flow Units and Hybrid Soft Computing Systems," SPE 71455.
16. Weiss, W.W., Wo, S., Weiss, J.W. and Weber, J.: "Data Mining at a Regulatory Agency to Forecast Waterflood Recovery" SPE 71057
17. Balch, R.S., Broadhead, R. F., Weiss, W.W. and Hart, D. M.: "Regyonal Data Analysis to Better Predict Drilling Success: Brushy Canyon Formation", SPE 75145
18. Weiss, W.W. and Balach, R.S.: "How Artificial Intelligence Methods can Forecast Oil Production", SPE 75143
19. Gauthier et al.: "Integrated Fractured Reservoir Characterization: a Case Study in a North Africa Field.", SPE 65118.
20. Aissaoui, K.: "Contribution of Horizontal Well Logs and Pressure Data in Stochastic Modeling." MS Thesis, Norman, Oklahoma 2001.