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Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study
Joint Authors
Ren, Hongjia
Wang, Xianchang
Ren, Hongbo
Guo, Qiulin
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration.
Determining the location of hydrocarbon is a complex and uncertain decision problem.
This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network.
In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method.
The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China.
The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map.
The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits.
American Psychological Association (APA)
Ren, Hongjia& Wang, Xianchang& Ren, Hongbo& Guo, Qiulin. 2020. Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1195570
Modern Language Association (MLA)
Ren, Hongjia…[et al.]. Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study. Mathematical Problems in Engineering No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1195570
American Medical Association (AMA)
Ren, Hongjia& Wang, Xianchang& Ren, Hongbo& Guo, Qiulin. Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1195570
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195570