Study on Artificial Neural Networks to Identify Sedimentary Microfacies

Abstract:

Based on a number of stratigraphic sedimentary information included in log data, application of the Artificial Neural Network to identify sedimentary microfacies from well logging data can complete the series auto-interpreting. The application can improve the auto-interpreting accuracy and make us get more satisfied results. Ten parameters from the well logging curves are selected for to describing their shape characteristics when the deposition patterns of 8 in gas-bearing formation of Upper Paleozoic group, Ordos basin are studied. Effective parameters were selected on the basis of cores, and based on artificial neural network pattern recognition technique; the sedimentary microfacies of well cross section were auto-interpreted. About 300 wells and the results were interpreted by using the software. The software will be fit for the researchers who have the experiences of geological interpretation and some backgrounds of local geology.

• Periodical: Advanced Materials Research (Volumes 912-914)
• Pages: 1395-1398
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.912-914.1395
• Online since: April 2014
• Authors: Wei Fu Liu*, Shuang Long Liu, Li Xin Sun
• Keywords: Logging Data, Neural Network (NN), Pattern Recognition, Sedimentary Nicrofacies

References

[1] D. G. Li, J. R. Ning and Z. F. Liu. Oil ＆ Gas Geology Vol. 31 (2010), p.493.

[2] Ｒ. J. Chong, X. H. Yu and T. G. Li. Oil ＆ Gas Geology Vol. 33 (2012), p.94.

[3] F. Xiao and X. P. Gao. Journal of Software Vol. 17 (2006), p.1042.

[4] Y. Lei, F. Q. Wang, H. J. Liu, et al. Natural Science Edition Vol. 27 (2012), p.27.

[5] M. Hu and H. Wamg. Tsinghua University Sci. and Tec. Vol. 12 (2007), p.26.

[6] L. Q. Yang. Progress in Geophysics Vol. 20 (2005), p.34.

[7] L. Guo, et al. Journal of Harbin Institute of Tec. Vol. 41 (2009), p.62.

[8] S. T. Fu and J. X. Nan. Journal of Palaeogeography Vol. 12 (2010), p.607.