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Open AccessDOI: 10.1016/j.ijmst.2025.07.010Original Research

Advances in thermo-hydro-mechanical-chemical modelling for CO2 geological storage and utilization

Nanlin Zhang¹,Liangliang Jiang¹,Fushen Liu¹,Yuhao Luo¹,Lele Feng¹,Yiwen Ju¹,Allegra Hosford Scheirer¹,Jiansheng Zhang¹,Birol Dindoruk¹,S.M. Farouq Ali¹,Zhangxin Chen¹

School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China

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Advances in thermo-hydro-mechanical-chemical modelling for CO2 geological storage and utilization
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Academic Research Journal
Published:January 15, 2025Edition:Vol. 32, Issue 7 • pp. 100-112Citation:Nanlin Zhang et al. (2025), Academic Research Journal
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Key Takeaways & Executive Findings

  • • THMC coupling processes are critical for CO2 storage integrity and efficiency, requiring advanced numerical models. • Fully coupled, iteratively coupled, and explicitly coupled solution methods offer different trade-offs between accuracy and computational cost. • Dynamic changes in porosity, permeability, and fracture evolution are essential to capture multi-field interactions. • Comparative evaluation of TOUGH, CMG-GEM, and COMSOL highlights their capabilities and limitations for CO2 storage simulation.
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Abstract

Geological storage and utilization of CO2 involve complex interactions among Thermo-hydro-mechanical-chemical (THMC) coupling processes, which significantly affect storage integrity and efficiency. To address the challenges in accurately simulating these coupled phenomena, this paper systematically reviews recent advances in the mathematical modeling and numerical solution of THMC coupling in CO2 geological storage. The study focuses on the derivation and structure of governing and constitutive equations, the classification and comparative performance of fully coupled, iteratively coupled, and explicitly coupled solution methods, and the modeling of dynamic changes in porosity, permeability, and fracture evolution induced by multi-field interactions. Furthermore, the paper evaluates the capabilities, application scenarios, and limitations of major simulation platforms, including TOUGH, CMG-GEM, and COMSOL. By establishing a comparative framework integrating model formulations and solver strategies, this work clarifies the strengths and gaps of current approaches and contributes to the development of robust, scalable, and mechanism-oriented numerical models for long-term prediction of CO2 behavior in geological formations.

1. Introduction

Global CO2 emissions reached a record high of 36.8 Gt in 2022 [1], and the atmospheric concentration of CO2 has dramatically surged over the past century due to substantial emissions. Its contribution to the greenhouse effect is now a scientific consensus. However, improving energy efficiency alone cannot fundamentally eliminate CO2 emissions, and the large-scale application of renewable energy technologies faces challenges in the short term. Therefore, CO2 capture, utilization, and storage (CCUS) represent the most direct and critical technological approach for reducing CO2 emissions and achieving carbon neutrality goals, providing essential support for these objectives [2].

This technology encompasses key processes such as CO2 capture, transportation, geological storage, and utilization. Geological storage and utilization are core components of CCUS technology, offering advantages of large storage volumes, long storage durations, and enhanced development of deep underground resources. These factors determine its development potential and direction, presenting an effective means for achieving carbon neutrality in the future. Numerous projects worldwide have entered the industrial demonstration or commercial application stage. CO2 geological storage and utilization involve injecting captured CO2 into geological formations through engineering technology, utilizing geological conditions to produce or enhance energy and resource extraction, and achieving the long-term or permanent isolation of CO2 from the atmosphere [3].

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Cite This Research Paper
Nanlin Zhang, Liangliang Jiang, Fushen Liu, Yuhao Luo, Lele Feng, Yiwen Ju, Allegra Hosford Scheirer, Jiansheng Zhang, Birol Dindoruk, S.M. Farouq Ali, Zhangxin Chen (2025). Advances in thermo-hydro-mechanical-chemical modelling for CO2 geological storage and utilization. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.07.010
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Frequently Asked Questions

What is THMC coupling in CO2 geological storage?

THMC coupling refers to the interactions among thermal (T), hydrological (H), mechanical (M), and chemical (C) processes that occur during CO2 injection and storage. These coupled processes significantly affect storage integrity and efficiency, and accurate modeling is essential for predicting long-term CO2 behavior.

What are the main numerical solution methods for THMC coupling?

The main numerical solution methods are fully coupled, iteratively coupled, and explicitly coupled approaches. Fully coupled methods solve all equations simultaneously, offering high accuracy but high computational cost. Iteratively coupled methods solve each process sequentially and iterate until convergence, balancing accuracy and efficiency. Explicitly coupled methods use explicit time stepping, which is simpler but may require smaller time steps for stability.

Which simulation platforms are commonly used for CO2 storage modeling?

Commonly used simulation platforms include TOUGH, CMG-GEM, and COMSOL. TOUGH is widely used for geothermal and multiphase flow simulations, CMG-GEM is a reservoir simulator with advanced geomechanical capabilities, and COMSOL provides a flexible multiphysics environment for custom THMC models.

Why is modeling dynamic porosity and permeability important?

Dynamic changes in porosity and permeability occur due to mineral dissolution/precipitation, stress changes, and fracture evolution. These changes directly affect CO2 injectivity, storage capacity, and seal integrity, so capturing them is crucial for realistic long-term predictions.

What are the key challenges in THMC modeling for CO2 storage?

Key challenges include accurately representing coupled processes across different scales, handling complex fracture networks, and managing computational costs. Additionally, data scarcity for model calibration and validation remains a significant hurdle.

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