Bayesian Hierarchical Modeling: Application Towards Production Results in the Eagle Ford Shale of South Texas

Se Yoon Lee () and Bani K. Mallick ()
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Se Yoon Lee: Texas A & M University
Bani K. Mallick: Texas A & M University

Sankhya B: The Indian Journal of Statistics, 2022, vol. 84, issue 1, No 1, 43 pages

Abstract: Abstract Recently, the petroleum industry has faced the era of data explosion, and many oil and gas companies resort to data-driven approaches for unconventional field development planning. The objective of this paper is to analyze shale oil wells in a shale reservoir and develop a statistical model useful for upstream. Shale oil wells dataset comprises three aspects of information: oil production rate time series data; well completion data; and well location data. However, traditional decline curve analysis only utilizes the temporal trajectory of the production rates. Motivated by this, we propose a Bayesian hierarchical model that exploits the full aspects of the shale oil wells data. The proposed model provides the following three functionalities: first, estimations of a production decline curve at an individual well and entire reservoir levels; second, identification of significant completion predictors explaining a well productivity; and third, spatial predictions for the oil production rate trajectory of a new well provided completion predictors. As a fully Bayesian approach has been adopted, the functionalities are endowed with uncertainty quantification which is a crucial task in investigating unconventional reservoirs. The data for this study come from 360 shale oil wells completed in the Eagle Ford Shale of South Texas.

Keywords: Bayesian hierarchical modeling; Decline curve analysis; Shale oil wells; Latent kriging; Primary 62F15; Secondary 62H11; 62M20 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13571-020-00245-8

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