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Estimation of missing logs by regularized neural networks

¹ Saudi Aramco, P.O. Box 9863, Dhahran, 31311, Saudi Arabia; msaggaf{at}alum.mit.edu
² Saudi Aramco, P.O. Box 12323, Dhahran, 31311, Saudi Arabia; edgardo.nebrija{at}aramco.com

Muhammad Saggaf has a B.S. degree from King Fahd University of Petroleum and Minerals and an M.S. degree and a Ph.D. from Massachusetts Institute of Technology. He joined Saudi Aramco in 1989 and has worked in exploration, reservoir characterization, and research and development. Along the way, he was awarded the Technology Achievement Award in 1997, the Creative Contribution Award in 2001, the SEG J. Clarence Karcher Award in 2001, and a patent for fractal deconvolution in 2002. He is a member of the Society of Exploration Geophysicists and AAPG, an elected member of the European Academy of Sciences, and the president of the Dhahran Geoscience Society.Ed Nebrija has a B.S. degree in physics from the University of the Philippines and a Ph.D. in geophysics from the University of Wisconsin-Madison. From 1979 to 1992, he worked for Shell Oil (United States) in various capacities as marine seismic party chief, explorationist, and reservoir geophysicist. He is currently a consultant geophysicist at Saudi Aramco, specializing in the reservoir characterization of offshore and onshore oil and gas fields. He is a member of the Society of Exploration Geophysicists and the European Association of Geoscientists and Engineers.

An approach based on regularized back-propagation neural networks can be used to estimate the missing logs, or parts of those logs, in wells with incomplete log suites. This is done by first analyzing the interdependence of the various log types in a training well that has a complete suite of logs, and then applying the network to nearby wells whose log suites are incomplete to estimate the missing logs in these wells. The accuracy of the method is evaluated by blind tests conducted on real well-log data. These tests indicate that the method produces accurate estimates that are close to the measured log values, and the method can thus be an effective means of enhancing limited suites of wire-line logs. Moreover, this approach has several advantages over the ad hoc practice of manually patching the missing logs from the complete log suites of proximate wells because it is automatic, objective, completely data driven, inherently nonlinear, and does not suffer from the overfitting difficulties commonly associated with conventional back-propagation networks. Additionally, it seems that an accurate selection of the optimal input log types is not necessary because redundant input containing several logs yields reasonably accurate results as long as some of the logs in the input are sufficiently correlated with the missing log.