Obtain an Optimum Artificial Neural Network Model for Reservoir Studies

Proceedings - SPE Annual Technical Conference and Exhibition

Volumn , Issue , 2003, Pages 3449-3455

  Du, Yuqi ^a   Weiss, W W ^b   Xu, Jianyun ^c   Balch, R S ^c   Li, Dacun ^d  

^a ChevronTexaco Company

^b Correlations Company ^*  (United States)

^c NEW MEXICO INSTITUTE OF MINING AND TECHNOLOGY   (United States)

^d UNIVERSITY OF HOUSTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE LANGUAGES; NON-LINEAR PARAMETERS;

COMPUTER SIMULATION; ERROR ANALYSIS; FUZZY SETS; GEOPHYSICS; LEARNING ALGORITHMS; NEURAL NETWORKS; PETROLEUM RESERVOIRS;

PETROLEUM INDUSTRY;

EID: 1142300779     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. JacekM. Zurada: Introduction to Artificial Neural Systems, West Publishing Company, 1995.
2. Baldwin et al.: "Application of a Neutral Network to the Problem of Mineral Identification from Well Logs," The Log Analyst, Vol.31, No. 5: 279-193, 1990.
3. Harrigan, et al: "Seismic Horizon Picking Using and Artificial Neural Network," ICASSP-92: presented at the IEE International Conference on Acoustics, Speech and Signal Processing, 1992.
4. Huang, et al: "A hybrid Neural Network for Seismic Pattern Recognition," presented at the IEE International Joint Conference on Neural Networks, Baltimore, 1992.
5. Balch R.S. et al: "Using Artificial Intelligence to Correlate Multiple Seismic Attributes to Reservoir Properties," SPE 56733, 1999.
6. Zhou Chengdang et al.: "Application of Back-propagation Neural Networks and Simulated Annealing Technique to Well Log Interpretation," Well Logging Technology, Vol. 17, No.4, 1993.
7. Derek et al.: "Comparative Study of Backpropagation Neural Network and Statistical Pattern Recognition Techniques in Identifying Sandstone Lithofacies". Presented at the 1990 Conference on Artificial Intelligence in Exploration and Production, Texas A&M: 41-49.
8. Poulton, M.M., et al: "Location of Subsurface Targets in Geophysical Data Using Neural Networks," Geophysics, Vol. 57, No. 12, 1992.
9. Mohaghegh, S., Arefi, R., and Ameri, S.: "A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks," SPE 28394, 1994.
10. Wiener et al: "Predicting Carbonate Permeabilities from Wireline Logs Using a Backpropagation Neural Network," presented at the 1993 Conference on Artificial Intelligence in Petroleum Exploration and Production, Texas, 1993.
11. Weiss, W.W et al: "Integrating Core Porosity and Sw Measurements with Log Values," SPE 55642, 1999.
12. Wo, S.: "A New Technique to Determine Porosity and Deep Resistivity from Old Gamma Ray and Neutron Count Logs," SPE 59553, 2000.
13. Balch, R.S.: "Predicting Core Porosity Using Wire-line Logs at Dagger Draw Field, SE New Mexico," SPE 59554, 2000.
14. Ariza, M., et al: "Automatic Fracture Detection from High Resolution Wellbore Images Using Artificial Neural Networks," presented at the Conference on Artificial Intelligence in Exploration and Production, Texas A&M, 1991.
15. Chawathe, A. Ouenes, Weiss W.W.: "Interwell Property Mapping Using Crosswell Seismic Attributes," SPE 38747, 1997.
16. Weiss W.W. et al: "Estimating Bulk Volume Oil in Thin-Bedded Turbidites," SPE 70041, 2001.
17. Epping, W.J.M.: "A Neural Network for Analysis and Improvement of Gas Well Productions," Presented at the Computer Simulation Conference, Calgary, 1990.
18. Doraisamy: "Methods of Neuro-Simulation for Field Development, " SPE 39962, 1998.
19. Wo, S., Weiss, W.W.: "Producing GOR Used to Predict Permeability Distribution in a Tight Heterogeneous Reservoir," SPE 56505, 1999.
20. Centilmen: "Applications of Neural Networks in Multiwell Field Development," SPE 56433 presented at the 1999 SPE Annual Technical Conference and Exhibition, Houston, 1999.
21. Mohaghegh, S., Balan, B., and Ameri, S.: "A Hybrid, Neuro-Genetic Approach to Hydraulic Fracture Treatment Design and Optimization," SPE 36602, 1996.
22. J.L. Baldwin et al: "Computer Emulation of Human Mental Process: Application of Neural Network Simulators to Problems in Well Log Interpretation," Halliburton Logging Services, SPE 19619, 1989.
23. Ershagi, et al.: "A Robust Neural Network Model for Pattern Recognition of Pressure Transient Test Data," SPE 26427.
24. Accarain, P.: "Neuro-computing Helps Pore Pressure Determination," Petroleum Engineer International, 1993.
25. Tapias O.: "Reservoir Engineer and Artificial Intelligence Techniques for Data Analysis," SPE 68743, 2001
26. He Zhong et al: "Neural-Network Approach To Predict Well Performance Using Available Field Data," SPE 68801, 2001.
27. Weiss W.W. et al: "Data Mining at a Regulatory Agency to Forecast Waterflood Recovery," SPE 71057, 2001.
28. Lou Mendelsohn: "Training Neural Networks," Technical Analysis of Stocks & Commodities, 1993.
29. Bohumir Jelinek: Neural Network Application Pattern Classification Problems, Institute for Signal and Information Processing, Mississippi Statte University, 2002.
30. Zhao, Ruihuan, Et al: "Application of the Artificial Neural Network in a Study of the Relationship Between Retention Index and Molecular Structure Parameters in MECC," American Laboratory, 2000.
31. http://ford.nmt.edu/PredictOnline6/index.html
32. Subramaniam V., and Weiss, W.W.: " Evaluation of Well Completion Opportunities in the Lower Brushy Canyon Using Neural Networks," Transactions, Southwest Section A.A.P.G Convention, Ruidoso, NM June 6-8, 2002.