Well-log correlation using a back-propagation neural network

Stefan M. Luthi1, Ian D. Bryant2
1Services Techniques Schlumberger, Montrouge, France
2Schlumberger-Doll Research, Ridgefield

Tóm tắt

We present a back-propagation neural network with an input layer in the form of a tapped delay line wich can be trained effectively on one or several well logs to recognize a particular geological marker. Subsequently, the neural network proposes locations of this marker on other wells in the field. Another neural network, similar in architecture to the first one, performs the same task for secondary markers using, in addition to the well logs, a depth reference function to the first marker. This method is shown to have better performance and better discrimination than standard cross-correlation techniques. It lends itself well for an interactive implementation on a workstation.

Tài liệu tham khảo

Bishop, C. M., 1995, Neural networks for pattern recognition: Clarendon Press, Oxford, 485 p.

Gamero de Villarroel, H., Higgs, R., Bryant, I., Baygun, B., and Herron, M., 1995, Application of sequence stratigraphie re-interpretation of Lower Lagunillas Member to further development of bloque IV, Lake Maracaibo, Venezuela. Am. Assoc. Petroleum Geologists, Ann. Conv. Abstracts (Houston, Texas), p. 31A.

Haykin, S., 1994, Neural networks: MacMillan College Publ. Co., New York, 696 p.

Hecht-Nielsen R., 1990, Neurocomputing: Addison-Wesley Publ. Co., Reading, Massachusetts, 433 p.

Kuo, T.-B., 1986, Well log correlation using artificial intelligence: unpubl. doctoral dissertation, Texas, A&M Univ., 138 p.

Lineman, D. J., Mendelson, J. D., and Toksoz, M. N., 1987, Well to well correlation using knowledge-based systems and dynamic depth warping: Trans. SPWLA 28th Ann. Logging Symp., Paper UU, 25 p.

McCormack, M., 1991, Neural computing in geophysics: The Leading Edge, v. 10, no. 1, p. 11–15.

Moran, J. H., Confleau, M. A., Miller, G. K., and Timmons, J. P., 1962, Automatic computation of dipmeter logs digitally recorded on magnetic tapes: Jour. Petroleum Technology, v. 16, no. 7, p. 771–782.

Press, W. H., Teulosky, S. A., Vetterling, W. T., and Flannery, B. P., 1992, Numerical Recipes: Cambridge, 963 p.

Smith, M., Carmichael, N., Reid, I., and Bruce, C, 1991, Lithofacies determination from wireline log data using a distributed neural network: Edinburgh Parallel Computing Centre, Rept. TR91-10, unpaginated.

Wiener, J. M., Rogers, J. A., and Moll, R. F., 1995, Predict permeability from wireline logs using neural networks: Petroleum Engineer Intern., v. 67, no. 5, p. 18–24.

Wu, X., and Nyland, E., 1987, Automated stratigraphic interpretation of well logs data: Geophysics, v. 52, no. 12, p. 1665–1676.