# THMC Modeling for CO2 Geological Storage: Advances, Challenges, and Prospects

## TL;DR

This paper reviews how thermal, hydraulic, mechanical, and chemical processes interact in CO2 geological storage and suggests ways to improve modeling for safer and more efficient carbon storage.

## Contribution

The paper provides the first comprehensive review of THMC coupling in CO2 geological storage and proposes development ideas like intelligent algorithms and multiscale modeling.

## Key findings

- Current THMC research is fragmented, lacking a unified multiphysics framework.
- THMC models are applied in site screening, wellbore integrity, and storage forecasts.
- Challenges include computational efficiency, parameter uncertainty, and validation issues.

## Abstract

CO2 geological storage (CGS) is an important
way to
reach carbon neutrality. Its long-term safety and effectiveness depend
on the interplay of thermal, hydraulic, mechanical, and chemical (THMC)
processes. However, contemporary research frequently examines discrete
processes or particular geological contexts, resulting in a fragmented
understanding of THMC interactions and the absence of a unified framework
for multiphysics simulation. This paper presents the first comprehensive
review of the application landscape of THMC coupling in CGS. It elucidates
the fundamental processes of each physical field and their interacting
mechanisms, spanning from two- to four-field coupling. It also looks
at the pros and cons of common numerical tactics, computational methodologies,
and software platforms, as well as how useful they are. It looks at
the most important uses of THMC models in site screening, wellbore
integrity evaluation, and storage evolution forecasts using common
situations such as deep saline aquifers and depleted oil and gas reservoirs.
This paper talks about current problems, such as computing efficiency,
parameter uncertainty, and lack of validation. It does this by suggesting
eight development ideas, such as using intelligent algorithms, multiscale
modeling, data assimilation, and standardized platforms. This evaluation
encourages a better understanding of how things work, better engineering
techniques, and the growth of CGS into a safer, more efficient, and
larger-scale application.