Version 1
: Received: 17 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (09:04:14 CET)
How to cite:
Du, X.; Salasakar, S.; Thakur, G.C. A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-EOR Projects. Preprints2024, 2024021118. https://doi.org/10.20944/preprints202402.1118.v1
Du, X.; Salasakar, S.; Thakur, G.C. A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-EOR Projects. Preprints 2024, 2024021118. https://doi.org/10.20944/preprints202402.1118.v1
Du, X.; Salasakar, S.; Thakur, G.C. A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-EOR Projects. Preprints2024, 2024021118. https://doi.org/10.20944/preprints202402.1118.v1
APA Style
Du, X., Salasakar, S., & Thakur, G.C. (2024). A Comprehensive Summary of the Application of Machine Learning Techniques for CO<sub>2</sub>-EOR Projects. Preprints. https://doi.org/10.20944/preprints202402.1118.v1
Chicago/Turabian Style
Du, X., Sameer Salasakar and Ganesh C Thakur. 2024 "A Comprehensive Summary of the Application of Machine Learning Techniques for CO<sub>2</sub>-EOR Projects" Preprints. https://doi.org/10.20944/preprints202402.1118.v1
Abstract
In recent years, machine learning (ML) techniques have emerged as an efficient and effective technology within the petroleum industry. This paper focuses on the current application of ML in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits of climate-change mitigation strategy. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure-Volume-Temperature (PVT) properties estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. In this comprehensive review, we catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML building process, and analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
