Quick Search:    advanced search

GSW Home   GeoRef Home   My GSW Alerts   Contact GSW   About GSW   Journals List   Help

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (5) Citing Articles via Google Scholar Google Scholar Articles by Saggaf, M. M. Articles by Nebrija, E. L. Search for Related Content GeoRef GeoRef Citation

Estimation of Lithologies and Depositional Facies from Wire-Line Logs

¹ Massachusetts Institute of Technology, MIT E34-462, 42 Carleton Street, Cambridge, Massachusetts 02142; msaggaf{at}alum.mit.edu
² Saudi Arabian Oil Company, P.O. Box 12323, Dhahran 31311, Saudi Arabia; nebrijel{at}aramco.com.sa

M. M. Saggaf has a B.Sc. in mathematics (1989) from King Fahd University of Petroleum and Minerals and an M.Sc. (1996) and Ph.D. (2000) in geophysics from the Massachusetts Institute of Technology (MIT). He has been working for Saudi Aramco in the Reservoir Characterization and R&D departments since 1989. His areas of interest include signal analysis, wave propagation, inversion, reservoir characterization, neural networks, and fuzzy logic. He is a member of AAPG and the Society of Exploration Geophysicists.Ed L. Nebrija has a B.Sc. in physics from University of the Philippines and a Ph.D. in geophysics form the University of Wisconsin-Madison. From 1979 to 1992, he worked for Shell Offshore, Inc. in New Orleans, Louisiana, in various capacities as marine seismic party chief, exploration geophysicist, and exploitation geophysicist. Since 1992, he has been a staff member of the Reservoir Characterization Department of Saudi Aramco. He is a member of AAPG, the Society of Exploration Geophysicists, and the European Association of Geoscientists and Engineers.

We approach the problem of identifying facies from well logs through the use of neural networks that perform vector quantization of input data by competitive learning. The method can be used in either an unsupervised or supervised manner. Unsupervised analysis is used to segregate a well into distinct facies classes based on the log behavior. Supervised analysis is used to identify the facies types present in a certain well by making use of the facies identified from cores in a nearby well. The method is suitable for analyzing lithologies and depositional facies of horizontal wells, which are almost never cored, especially if core data is available for nearby vertical wells. Both modes are implemented and used for the automatic facies analysis of horizontal wells in Saudi Arabia. In addition to the identification of facies, the method is also able to calculate, for each analysis, confidence measures that are indicative of how well the analysis procedure can identify those facies given uncertainties in the data. Moreover, we can apply constraints derived from human experience and geologic principles to guide the inference process.

This article has been cited by other articles:

				Estimation of missing logs by regularized neural networks AAPG Bulletin, August 1, 2003; 87(8): 1377 - 1389.

				A fuzzy logic approach for the estimation of facies from wire-line logs AAPG Bulletin, July 1, 2003; 87(7): 1223 - 1240.

				A. Bhatt and H. B. Helle Determination of facies from well logs using modular neural networks Petroleum Geoscience, September 1, 2002; 8(3): 217 - 228. [Abstract] [Full Text] [PDF]