About This Item
- Full TextFull Text(subscription required)
- Pay-Per-View PurchasePay-Per-View
Purchase Options Explain
Share This Item
The AAPG/Datapages Combined Publications Database
AAPG Bulletin
Abstract
AAPG Bulletin, V.
Estimation of lithologies and depositional facies from wire-line logs
M. M. Saggaf,1 Ed L. Nebrija2
1Massachusetts Institute of Technology, MIT E34-462, 42
Carleton Street, Cambridge, Massachusetts 02142; email: [email protected]
2Saudi Arabian Oil Company, P.O. Box 12323, Dhahran 31311, Saudi Arabia; ;
email: [email protected]
AUTHORS
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 RD 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 WisconsinMadison. 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.
ACKNOWLEDGMENTS
We would like to thank Saudi Arabian Oil Company (Saudi Aramco) for supporting this research and for granting us permission for its publication.
ABSTRACT
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.
Pay-Per-View Purchase Options
The article is available through a document delivery service. Explain these Purchase Options.
| Watermarked PDF Document: $14 | |
| Open PDF Document: $24 |
AAPG Member?
Please login with your Member username and password.
Members of AAPG receive access to the full AAPG Bulletin Archives as part of their membership. For more information, contact the AAPG Membership Department at [email protected].