Bibliography of Well-Log Applications
1996 Annual Update

1. Ali, J.K., and Fawcett, M.J., 1996, A knowledge-based system for the identification and generation of relative permeabilities of reservoir rocks, SPE-36009, in SPE international symposium on formation damage control proceedings: Society of Petroleum Engineers, p. 219-228.
2. Askida, Y., 1995, Well-log analysis by use of neural network, paper O, in 1st annual well logging symposium of Japan, proceedings: Society of Professional Well Log Analysts, Japan Chapter, 7 p.
3. Einstein, E.E., and Edwards, K.W., 1988, Comparison of an expert system to human experts in well log analysis and interpretation, SPE-18129, in Annual technical conference proceedings, v. omega, formation evaluation and reservoir geology: Society of Petroleum Engineers, p. 253-262. Later published in 1990: SPE Formation Evaluation, v. 5, no. 1, March, p. 39-45. Later reprinted in 1996, in Expert system in engineering applications: Society of Petroleum Engineers Reprint Series No. 41, p. 93-99.
4. Huang, Z., Shimeld, J., Williamson, M., and Katsube, J., 1996, Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada: Geophysics, v. 61, no. 2, p. 422-436.
5. Huang, Z., and Williamson, M.A., 1996, Artificial neutral network modelling as an aid to source rock characterization: Marine and Petroleum Geology, v. 13, no. 2, p. 277-290.
6. Imamura, S., 1996, Log interpretation using fuzzy theory, paper C, in 2nd annual well logging symposium of Japan, proceedings: Society of Professional Well Log Analysts, Japan Chapter, 7 p.
7. Loskiewicz, J., and Swakon, J., 1995, Using artificial neural networks to assist in the interpretation of geophysical data: Acta Geophysica Polonica, v. 43, no. 3, p. 231-245.
8. Malki, H.A., Baldwin, J.L., and Kwari, M.A., 1995, Estimating permeability by use of neural networks in thinly bedded shaly gas sands, SPE-31010: Society of Petroleum Engineers, unsolicited manuscript,. Later published in 1996: SPE Computer Applications, v. 8, no. 2, p. 58-62.
9. Peveraro, R.C.A., and Lee, J.A., 1988, HESPER--An expert system for petrophysical formation evaluation, paper R, in 11th European formation evaluation symposium transactions: Society of Professional Well Log Analysts, Norwegian Chapter, 22 p. Also published in 1988, as SPE-18375, in SPE European petroleum conference [London] proceedings: Society of Petroleum Engineers, p. 361-370. Later reprinted in 1996, in Expert system in engineering applications: Society of Petroleum Engineers Reprint Series No. 41, p. 100-109.
10. Rieuwerts, H., 1989, GEOLOGIX--an interactive knowledge-based well correlation system, in Conference on artificial intelligence in petroleum proceedings: Texas A&M University, College Station, p. 177. Later reprinted in 1996, in Expert system in engineering applications: Society of Petroleum Engineers Reprint Series No. 41, p. 110-119.
11. Rogers, S.J., Chen, H.C., Kopaska-Merkel, D.C., and Fang, J.H., 1995, Predicting permeability from porosity using artificial neural networks: AAPG Bulletin, v. 79, no. 12, p. 1786-1797.
12. Smith, R.G., and Baker, J.D., 1983, The Dipmeter Advisor system, a case study in commercial expert system development, in 8th international joint conference on artificial intelligence, proceedings: International Joint Conference on Artificial Intelligence, p. 122-129. Later reprinted in 1996, in Expert system in engineering applications: Society of Petroleum Engineers Reprint Series No. 41, p. 38-45.
13. Startzman, R.A., and Kuo, T.B., 1986, An artificial intelligence approach to well log correlation, paper WW, in 27th annual logging symposium transactions: Society of Professional Well Log Analysts, 21 p. Also published in 1986 as, A rule-based system for well log correlation, SPE-15295, in Symposium on petroleum industry applications of microcomputers [Silver Creek, Colorado, June 18-20], proceedings: Society of Petroleum Engineers, p. 113-124. Later published in 1987: The Log Analyst, v. 28, no. 2, March-April, p. 175-183, and SPE Formation Evaluation, v. 2, no. 3, September, p. 311-319. Later reprinted in 1996, in Expert system in engineering applications: Society of Petroleum Engineers Reprint Series No. 41, p. 120-128.
14. Szucs, P., and Civan, F., 1996, Multi-layer well log interpretation using the simulated annealing method: Journal of Petroleum Science and Engineering, v. 14, p. 209-220.
15. White, A.C., Molnar, D., Aminian, K., Mohaghegh, S., Ameri, S., and Esposito, P., 1995, The application of ANN [artificial neural networks] for zone identification in a complex reservoir, SPE-30977, in Eastern region conference proceedings: Society of Petroleum Engineers, p. 27-32.
16. Wong, P.M., and Taggart, I.J., 1995, An improved technique in porosity prediction--a neural network approach: IEEE Transactions on Geoscience and Remote Sensing, v. 33, no. 4, July, p. 971-980.