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Vol.1 (2025) Iss. 1

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Abstract
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References
Review

A Review of Integrated CCUS-EOR and Storage Prediction and Optimization: From Accelerated Compositional Simulation to Data–Physics Coupled Intelligent Models

Yunfeng Xu1,2,*, Zhuyi Zhu1

1 School of Petroleum Engineering, Yangtze University, Wuhan, 430100, China
2 Western Research Institute, Yangtze University, Karamay 834000, China
* Correspondence: Yunfeng Xu, E-mail: 201972114@yangtzeu.edu.cn
 
AESIG, 2025, 1(1), 24-53;

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Abstract
 
Carbon dioxide enhanced oil recovery with geological storage has attracted increasing attention because it can simultaneously improve hydrocarbon recovery and reduce emissions. Accurate and efficient prediction of development performance, together with reliable support for injection–production design and optimization, has therefore become a central scientific and engineering challenge. Numerical simulation for carbon dioxide flooding has evolved from improved black-oil and pseudo-compositional formulations to K-value approaches and, more recently, equation-of-state-based compositional models. Although compositional simulation offers high mechanistic fidelity, it suffers from severe computational burdens in high-resolution three-dimensional models and in iterative workflows for history matching and optimization. To alleviate these constraints, three complementary acceleration routes have been developed, including multiscale methods that embed fine-scale heterogeneity into coarse-scale solutions, streamline-based methods that leverage convection-dominated flow characteristics, and reduced-order models that compress the state space for rapid iterative evaluation. In parallel, data-driven surrogate models have progressed rapidly with the growing availability of production, monitoring, and simulation data. These approaches enable fast forecasting, sensitivity analysis, and multi-objective decision support, yet their reliability remains limited under complex phase behavior and out-of-distribution operating conditions. Recent data–physics coupling paradigms, represented by simplified mechanistic models, network-based flow models, and physics-constrained deep-learning frameworks, provide promising pathways to reconcile physical consistency with computational efficiency. This review synthesizes the evolution, applicability boundaries, and engineering performance of these methods, and highlights future directions toward trustworthy, field-oriented intelligent simulation and closed-loop optimization in highly heterogeneous reservoirs.
 
Keywords: CCUS-EOR; numerical simulation; reduced-order models; deep learning; data–physics coupling; physics-informed neural networks.

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