What question did this study set out to answer?

The aim is to address the challenges in scaling CCUS technologies from laboratory to industrial applications.

April 16, 2026Energy & Fuels2 citations

Bridging the Scale Gap in CO 2 Geological Storage and Utilization: A Review of Core-Scale Mechanisms and Experimental Characterization

YSYankun SunChina University of Geosciences TSTianyu SunChina University of Geosciences ZDZijie DongChina University of Geosciences

Key Points

The aim is to address the challenges in scaling CCUS technologies from laboratory to industrial applications.
Conducted a systematic review of over 200 studies related to CO2 storage mechanisms.
Identified key mechanisms like fluid trapping and chemo-mechanical coupling affecting CO2 behavior.
Utilized advanced imaging techniques like X-ray CT and NMR for parameter calibration.
Proposed an integrated technical framework for modeling CO2 storage.
Highlighted the need for fractional advection-dispersion equations to capture transport behavior in carbonates.
Demonstrated robust insights for spatial heterogeneity in sealing integrity from lab experiments.
Introduced physics-informed neural networks to improve upscaling processes compared to traditional AI.

Abstract

Driven by the urgent need to mitigate global carbon emissions, carbon capture, utilization, and storage (CCUS) has emerged as a critical strategy for achieving carbon neutrality. Despite recent progress, bridging the scale gap between core-scale physics and macroscopic engineering design remains a formidable challenge. Following a systematic review of over 200 key studies (2015–2025), this work identifies the lack of rigorous parameter-mapping functions as a primary roadblock. To bridge this scale gap, this review systematically synthesizes the fundamental mechanisms of CO2 storage and utilization, specifically fluid trapping, chemo-mechanical coupling, and thermodynamic responses. Key findings demonstrate that pore-scale heterogeneity necessitates the transition from classical Fickian models to fractional advection-dispersion equations (fADE) to accurately capture the “long-tailing” transport in carbonates. It is evaluated how advanced in situ techniques, notably X-ray computed tomography (CT) and nuclear magnetic resonance (NMR), enable the precise calibration of critical parameters, including residual gas saturation (Sgr), hysteretic relative permeability, and mass transfer shape factors. Beyond parametrization, these laboratory-derived insights underpin several critical engineering benchmarks. Specifically, this study proposes an integrated technical framework: (i) a quantitative seal integrity margin supplemented by fracture-density analysis to account for spatial heterogeneity; (ii) the optimization of dimensionless parameters (Ca,Pe,Da) utilizing flux-weighted concentrations to estimate effective reaction rates (Daeff); and (iii) a stability criterion for EGS (Ri) derived from fracture heat-transfer dynamics. Furthermore, we demonstrate that physics-informed neural networks (PINNs) provide a more robust path for upscaling than traditional black-box AI by embedding governing PDEs into the loss function. Ultimately, this work provides robust experimental evidence and quantitative guidelines for site selection and risk mitigation, facilitating the critical transition of CCUS technologies from fundamental laboratory research to large-scale industrial deployment.

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