Application of Machine Learning for Shale Oil and Gas “Sweet Spots” Prediction

Hongjun Wang, Zekun Guo (), Xiangwen Kong, Xinshun Zhang, Ping Wang and Yunpeng Shan
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Hongjun Wang: The Research Institute of Petroleum Exploration & Development CNPC, Beijing 100083, China
Zekun Guo: The Research Institute of Petroleum Exploration & Development CNPC, Beijing 100083, China
Xiangwen Kong: The Research Institute of Petroleum Exploration & Development CNPC, Beijing 100083, China
Xinshun Zhang: The Research Institute of Petroleum Exploration & Development CNPC, Beijing 100083, China
Ping Wang: The Research Institute of Petroleum Exploration & Development CNPC, Beijing 100083, China
Yunpeng Shan: The Research Institute of Petroleum Exploration & Development CNPC, Beijing 100083, China

Energies, 2024, vol. 17, issue 9, 1-19

Abstract: With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for “sweet spots” prediction in shale oil and gas areas. Taking the Duvernay shale oil and gas field in Canada as an example, this paper attempts to build recoverable shale oil and gas reserve prediction models using machine learning methods and geological and development big data, to predict the distribution of recoverable shale oil and gas reserves and provide a basis for well location deployment and engineering modifications. The research results of the machine learning model in this study are as follows: ① Three machine learning methods were applied to build a prediction model and random forest showed the best performance. The R 2 values of the built recoverable shale oil and gas reserves prediction models are 0.7894 and 0.8210, respectively, with an accuracy that meets the requirements of production applications; ② The geological main controlling factors for recoverable shale oil and gas reserves in this area are organic matter maturity and total organic carbon (TOC), followed by porosity and effective thickness; the main controlling factor for engineering modifications is the total proppant volume, followed by total stages and horizontal lateral length; ③ The abundance of recoverable shale oil and gas reserves in the central part of the study area is predicted to be relatively high, which makes it a favorable area for future well location deployment.

Keywords: machine learning; random forest; main controlling factor analysis; sweet spot prediction; recoverable reserves (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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