机器学习在CO2提高油气采收率与地质封存中的研究进展

doi:10.13809/j.cnki.cn32-1825/te.2025268

Abstract

Abstract: Carbon Capture, Utilization, and Storage (CCUS) is a key technology for achieving carbon neutrality. It enables the dual benefits of increasing energy production and reducing CO₂ emissions through CO₂ enhanced oil and gas recovery (EOR/EGR) and geological storage. However, the large-scale application of CCUS technology faces technical challenges such as engineering design and risk assessment. Traditional solutions rely on empirical formulas, experimental verification, and physical models. When dealing with complex systems, these methods suffer from low computational efficiency, insufficient model accuracy, and difficulties in addressing multi-dimensional coupling issues.Machine Learning (ML), with its powerful data-driven analytical capabilities and adaptive optimization features, can train high-precision prediction models. It can optimize operating parameters, predict reservoir fluid behavior, assess leakage risks, and more, thereby enabling real-time monitoring and intelligent decision-making for complex systems and enhancing the safety and economic efficiency of CCUS technology.This study systematically reviews the applications of ML in CO₂-enhanced oil and gas recovery and geological storage. In terms of CO₂-enhanced oil and gas recovery, the applications cover seepage mechanism modeling, well pattern design optimization, production prediction and evaluation, multi-objective optimization, minimum miscibility pressure prediction, gas adsorption curve prediction, and CO₂-CH₄ diffusion assessment. In the aspect of CO₂ geological storage, the applications include reservoir selection, research on CO₂dissolution and diffusion mechanisms, geological storage effect prediction, and risk assessment.Existing studies show that ML exhibits significant advantages in improving prediction accuracy, optimizing operating parameters, and enhancing computational efficiency. It has made important progress in key fields such as reservoir selection, gas adsorption prediction, and storage effect prediction. Nevertheless, improvements are still needed in terms of adaptability to complex geological scenarios, model universality, dynamic data processing capabilities, and physical interpretability.

Key words: machine learning, intelligent algorithm, CCUS (carbon capture, utilization and storage), enhanced oil and gas recovery, geological storage

CLC Number:

TE357

YE HONGYING,CAO CHENG,ZHAO YULONG, et al. Research progress of machine learning in CO₂ enhanced oil and gas recovery and geological storage[J]. Petroleum Reservoir Evaluation and Development, 0, (): 0-.

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Research progress of machine learning in CO₂ enhanced oil and gas recovery and geological storage

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Research progress of machine learning in CO2 enhanced oil and gas recovery and geological storage

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Research progress of machine learning in CO₂ enhanced oil and gas recovery and geological storage