Insights into the Application of Machine Learning in Reservoir Engineering: Current Developments and Future Trends

Hai Wang and Shengnan Chen ()
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Hai Wang: Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Shengnan Chen: Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada

Energies, 2023, vol. 16, issue 3, 1-11

Abstract: In the past few decades, the machine learning (or data-driven) approach has been broadly adopted as an alternative to scientific discovery, resulting in many opportunities and challenges. In the oil and gas sector, subsurface reservoirs are heterogeneous porous media involving a large number of complex phenomena, making their characterization and dynamic prediction a real challenge. This study provides a comprehensive overview of recent research that has employed machine learning in three key areas: reservoir characterization, production forecasting, and well test interpretation. The results show that machine learning can automate and accelerate many reservoirs engineering tasks with acceptable level of accuracy, resulting in more efficient and cost-effective decisions. Although machine learning presents promising results at this stage, there are still several crucial challenges that need to be addressed, such as data quality and data scarcity, the lack of physics nature of machine learning algorithms, and joint modelling of multiple data sources/formats. The significance of this research is that it demonstrates the potential of machine learning to revolutionize the oil and gas sector by providing more accurate and efficient solutions for challenging problems.

Keywords: machine learning; oil and gas industry; production forecasting; well test analysis; reservoir characterization; challenges (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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