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

Se Yoon Lee, Bani Mallick

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.