A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects

Abstract

This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. 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) property estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. 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 the 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.

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

“…Examples of this category include the studies published by Dehaghani and Soleimani, Alatefi et al., Olofinnika et al, and Al-Khafaji et al Among the prior papers, the recent paper of Alatefi et al is the richest one as it summarizes many previous studies on the application of AI techniques for predicting the MMP of CO 2 – oil systems. A special section in review papers related to CCSU or the application of AI techniques in the oil and gas industry: some recent review papers included a special section or certain paragraphs devoted to discussing the implementation of AI techniques for modeling the MMP of the systems CO 2 – oil. Examples of this category include the review papers published by Du et al, Ng et al, Song et al, and Mao and Jahanbani Ghahfarokhi A part of a chapter in books devoted to the application of AI techniques in the petroleum industry or gas injection methods, such chapter four of the book published by Hemmati-Sarapardeh et al and the chapter written by Hadavimoghaddam et al …”

“…A special section in review papers related to CCSU or the application of AI techniques in the oil and gas industry: some recent review papers included a special section or certain paragraphs devoted to discussing the implementation of AI techniques for modeling the MMP of the systems CO 2 – oil. Examples of this category include the review papers published by Du et al, Ng et al, Song et al, and Mao and Jahanbani Ghahfarokhi …”