Machine learning and data mining assisted petroleum reservoir engineering: a comprehensive review. (23rd June 2022)

• Record Type: Journal Article
• Authors: Purbey, Rupali; Parijat, Harshwardhan; Agarwal, Divya; Mitra, Devarati; Agarwal, Rakhi; Pandey, Rakesh Kumar; Dahiya, Anil Kumar
• Is Part Of: International journal of oil, gas and coal technology. Volume 30:Number 4(2022)
• Page Start: 359
• Page End: 387
• Publication Date: 2022-06-23
• Subjects: machine learning -- data mining -- prediction model -- neural network -- statistical measure -- petroleum -- reservoir engineering -- performance prediction -- well test -- enhanced oil recovery; Petroleum as fuel -- Periodicals; Gas as fuel -- Periodicals; Coal -- Periodicals; Biomass energy -- Periodicals; Hydrogen as fuel -- Periodicals; 662.6
• Languages: English
• ISSNs: 1753-3309

Abstract

The oil and gas industry faces several challenges associated with managing massive datasets and extracting relevant information. The machine learning tools have proven to be significantly valuable for analysing complex, heterogeneous data and produce quicker and more reliable outcomes even on large-scales. Machine learning and data mining tools have been applied in several aspects of the upstream oil and gas industry, such as exploration, drilling, reservoir engineering, and production forecasting. This review has been explicitly focused on machine learning and data mining implementations in reservoir engineering, including reservoir characterisation and performance prediction, well test analysis, well logging and formation evaluation, and enhanced oil recovery operations. The commonly used statistical measures for classification and regression models have been discussed as well. The observations from the review have led to suitable suggestions that shall enrich the research in this area. [Received: July 29, 2021; Accepted: October 30, 2021]