Abstract In this study, we analyzed the geological, gravity, magnetic, and electrical characteristics of depressions in the Erlian Basin. Based on the results of these analyses, we could identify four combined feature parameters showing strong correlations and sensibilities to the reservoir oil-bearing conditions: the average residual gravity anomaly, the average magnetic anomaly, the average depth of the conductive key layer, and the average elevation of the depressions. The feature parameters of the 65 depressions distributed in the whole basin were statistically analyzed: each of them showed a Gaussian distribution and had the basis of Bayesian theory. Our Bayesian predictions allowed the definition of a formula to calculate the posterior probability of oil occurrence in the depressions based on the combined characteristic parameters. The feasibility of this prediction method was verifi ed by considering the results obtained for the 22 drilled depressions. Subsequently, we were able to determine the oilbearing threshold of hydrocarbon potential for the depressions in the Erlian Basin, which can be used as a standard for quantitative optimizations. Finally, the proposed prediction method was used to calculate the probability of hydrocarbons in the other 43 depressions. Based on this probability and on the oil-bearing threshold, the five depressions with the highest potential were selected as targets for future seismic explorations and drilling. We conclude that the proposed method, which makes full use of massive gravity, magnetic, electric, and geological data, is fast, effective, and allows quantitative optimizations; hence, it will be of great value for the comprehensive geophysical evaluation of oil and gas in basins with depression group characteristics.
The study supported by National Key R&D Program of China (No. 2018YFC0603302), Open Fund of Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education (Grant No. PI2018-01;K2017-23), the joint project of production, study and research sponsored by Huabei Oilfi eld Company, PetroChina.
Corresponding Authors: Yan Liang-Jun (E-mail: yljemlab@163.com).
E-mail: yljemlab@163.com
About author: Xu Feng-Jiao is currently a Ph.D. student in g e o d e t e c t i o n a n d i n f o r m a t i o n technology in Yangtze University. She obtained her M.S. in geodetection and information technology in Yangtze University in 2016. She is interested in integrated geophysical method study and application. E-mail:xfj2018@Hotmail.com. Address:Yangtze University (Wuhan Campus), No.111 University road, Caidian District, Wuhan City, Hubei, Province,430100.
Cite this article:
. Bayesian prediction of potential depressions in the Erlian Basin based on integrated geophysical parameters*[J]. APPLIED GEOPHYSICS, 2020, 17(3): 338-348.
2020, Vol. 17 
