Permeability estimation from well log responses

Journal of Canadian Petroleum Technology

Volumn 45, Issue 11, 2006, Pages 41-46

  Shokir, E M El M ^a   Alsughayer, A A ^a   Al Ateeq, A ^a  

^a KING SAUD UNIVERSITY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; MATHEMATICAL MODELS; MEASUREMENT THEORY; NEURAL NETWORKS; OIL FIELDS; OIL WELLS; PETROLEUM RESERVOIR EVALUATION;

FORMATION PERMEABILITY; HYDROCARBON BEARING FORMATIONS; NON-PARAMETRIC MODEL;

HYDROCARBONS;

EID: 33846131970     PISSN: 00219487     EISSN: None     Source Type: Journal    
DOI: 10.2118/06-11-05     Document Type: Article

References (18)

1. MOHAGHEGH, S., BALAN, B., and AMERI, S., Permeability Determination From Well Log Data; SPE Formation Evaluation, Vol. 12, No. 3, pp. 170-174, September 1997.
2. BATEMAN, R.M., Open-Hole Log Analysis and Formation Evaluation; International Human Resources Development Corp, Boston, MA, 1985.
3. WENDT, W.A., SAKURAI, S., and NELSON, P.H., Permeability Prediction From Well Logs Using Multiple Regression; in Reservoir Characterization, L.W. Lake and H.B. Caroll Jr. (eds.), Academic Press, New York, NY, pp. 181-222, 1985.
4. MOHAGHEGH, S., BALAN, B., and AMERI, S., State-of-the-Art in Permeability Determination From Well Log Data: Part 2-Verifiable, Accurate Permeability Predictions, the Touch-Stone of All Models; paper SPE 30979. presented at the SPE Eastern Regional Meeting, Morgantown, WV, September 18 - 20, 1995.
5. SIZE, W.B., Use and Abuse of Statistical Methods in the Earth Sciences; Oxford University Press, New York, NY, 1987
6. DAVIS, J.C., Statistics and Data Analysis in Geology; John Wiley & Sons. New York. NY. 1990.
7. SHOKIR, E.M. EL-M., Neural Network Determines Shaly-Sand Hydrocarbon Saturation; The Oil and Gas Journal, pp. 37-41, April 23, 2001.
8. rd Annual Conference of the IAMG, Part 2, Barcelona, Spain, pp. 675-680, September 22-27, 1997.
9. WONG, P.M., TAGGART, I.J., and JIAN, F.X., A Critical Comparison of Neural Networks and Discriminant Analysis in Lithofacies, Porosity, and Permeability Predictions; Journal of Petroleum Geology, Vol. 18. No. 2, pp. 191-206, 1995.
10. WONG, P.M., TAMHANE, D., and WANG, L., A Neural Network Approach to Knowledge-Based Well Interpolation: A Case Study of a Fluvial Sandstone Reservoir; Journal of Petroleum Geology, Vol. 20, No. 3, pp. 363-372, 1997.
11. SMITH, M., Neural Networks for Statistical Modelling; Van Nostrand Reinhold, New York, NY, p. 235, 1993.
12. DAYOFF, J.E., Neural Network Architectures: An Introduction; Van Nostrand Reinhold, New York, NY, p. 259, 1990.
13. MCCULLOCH, W.S. and PITTS, W.H., A Logical Calculus of the Ideas Immanent in Nervous Activity; Bulletin of Mathematical Biophysics, Vol. 5, pp. 115-133, 1943.
14. ROSENBLATT, F., The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain; Psychological Review, Vol. 65. No. 6. pp. 386-408. November 1958.
15. WIDROW, B. and HOFF, Jr., M.E., Adaptive Switching Circuits; In Institute of Radio Engineers Western Electric Show and Convention Convention Record, Volume 4, pp. 96-104, 1960.
16. MINSKY, M. and PAPERT, S., Perceptrons: An Introduction to Computational Geometry; MIT Press, Cambridge, MA, 1969
17. HOPFIELD, J.J., Neurons With Graded Response Have Collective Computational Properties Like Those of Two-State Neurons; proceedings of the National Academy of Sciences USA. Vol. 81, pp. 3088-3092. May 1984.
18. RUMELHART, D.E., HINTON, G.E., and MCCLELLAND, J.L., A General Framework for Parallel Distributed Processing; in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, D.E. Rumelhart and J.L. McClelland (eds.), MIT Press, Cambridge. MA, pp. 45-76, 1986.