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Original Paper | Open Access

Production decline curve analysis of shale oil wells: A case study of Bakken, Eagle Ford and Permian

Hui-Ying Tanga()Ge HeaYing-Ying NiaDa HuobYu-Long ZhaoaLiang XuecLie-Hui Zhanga()
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, 610500, Sichuan, China
Stanford University, Stanford, CA, USA
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, 102249, China

Edited by Yan-Hua Sun

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Abstract

The shale revolution has turned the United States from an oil importer into an oil exporter. The success of shale oil production in the U.S. has inspired many countries, including China, to begin the exploitation and development of shale oil resources. In this study, the production curves of over 30,000 shale oil wells in the Bakken, Eagle Ford (EF) and Permian are systematically analyzed to provide reference and guidance for future shale oil development. To find out the most suitable decline curve models for shale oil wells, fifteen models and a new fitting method are tested on wells with production history over 6 years. Interestingly, all basins show similar results despite of their varieties in geological conditions: stretched exponential production decline (SEPD) + Arps model provides most accurate prediction of estimated ultimate recovery (EUR) for wells with over 2 years' production, while the Arps model can be used before the two years’ switch point. With the EUR calculated by decline curve analysis, we further construct simple regression models for different basins to predict the EUR quickly and early. This work helps us better understand the production of shale oil wells, as well as provide important suggestions for the choices of models for shale oil production prediction.

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Petroleum Science
Pages 4262-4277
Cite this article:
Tang H-Y, He G, Ni Y-Y, et al. Production decline curve analysis of shale oil wells: A case study of Bakken, Eagle Ford and Permian. Petroleum Science, 2024, 21(6): 4262-4277. https://doi.org/10.1016/j.petsci.2024.07.029
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