Governing factors and mechanisms of CO<sub>2</sub> microscale sweep efficiency in shale reservoirs based on causal machine learning

JIANG Jiatong; ZHANG Yihang; SONG Zhaojie; YAN Ruisheng; ZHENG Lijun; ZHANG Kaixing; LI Peiyu; HUANG Shengjie; TANGPARITKUL Suparit

doi:10.12090/j.issn.1006-6616.2025116

Volume 32 Issue 1

Feb. 2026

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Article Navigation > Journal of Geomechanics > 2026 > 32(1): 258-271

JIANG J T，ZHANG Y H，SONG Z J，et al.，2026. Governing factors and mechanisms of CO2 microscale sweep efficiency in shale reservoirs based on causal machine learning[J]. Journal of Geomechanics，32（1）：258−271 doi: 10.12090/j.issn.1006-6616.2025116

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JIANG J T，ZHANG Y H，SONG Z J，et al.，2026. Governing factors and mechanisms of CO₂ microscale sweep efficiency in shale reservoirs based on causal machine learning[J]. Journal of Geomechanics，32（1）：258−271 doi: 10.12090/j.issn.1006-6616.2025116

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Governing factors and mechanisms of CO₂ microscale sweep efficiency in shale reservoirs based on causal machine learning

doi: 10.12090/j.issn.1006-6616.2025116

1.
School of Petroleum, Karamay Campus, China University of Petroleum (Beijing), Karamay 834000, Xinjiang, China
2.
National Key Laboratory of Oil and Gas Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China
3.
Huanxiling Oil Production Plant, PetroChina Liaohe Oilfield Company, Panjin 124114, Liaoning, China
4.
Research Institute of Oil and Gas Technology of PetroChina Changqing Oilfield Branch, Xi'an 710018, Shannxi, China
5.
Department of Mining and Petroleum Engineering, Chiang Mai University, Chiang Mai 50200, Thailand

Funds: This research was financially supported by the National Natural Science Foundation of China (Grant No. 52504050), the Corporate Innovation and Development Joint Fund Integrated Project of the National Natural Science Foundation of China (Grant No. U24B6002), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant Nos. 2024D01B96 and 2024B01015), the Karamay Innovation Environment Construction Plan (Grant No. 2025DB0150), and the Scientific Research Start-up Fund of the Karamay Subcampus of the China University of Petroleum, Beijing (Grant No. XQZX20250035).

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Received: 2025-08-21
Revised: 2025-11-30
Accepted: 2026-01-07

Available Online: 2026-01-13

Published: 2026-02-27

Abstract

Abstract

Objective During CO₂ fluid injection into oil reservoirs or saline aquifers, CO₂-water-rock interactions can alter porous media properties, thereby influencing the CO₂ microscale sweep efficiency, primarily due to capillary effects. Laboratory experiments and micro/nanoscale numerical simulations often struggle to isolate the specific contributions of individual pore properties, limiting targeted injection optimization for maximizing geological storage potential. Methods To investigate the dynamic evolution of pore structures and properties during multi-mineral competitive dissolution-precipitation reactions under CO₂ injection, we developed a lattice Boltzmann method (LBM). This method made it possible to simulate CO₂-water-rock interactions in shale oil reservoirs and to analyze pore properties (e.g., average wettability, roughness, porosity) and CO₂ microscale sweep efficiency. The LBM simulations generated a dataset covering various pore property scenarios to support causal machine learning. Using a double machine learning framework with a random forest algorithm, a causal inference prediction model was built for CO₂ microscale sweep efficiency, treating reaction time as a continuous treatment variable. Results This model quantified the relative importance of key pore parameters—porosity, wettability, and mean pore diameter—on sweep efficiency within the pore network. Its results indicate that reservoirs with higher proportions of carbonate minerals (calcite) exhibit greater CO₂ microscale sweep efficiency. The CO₂-water-rock reaction triggers calcite dissolution, forming preferential flow paths, while the secondary precipitation of oil-wet calcite induces localized wettability alteration. This dynamic "dissolution–secondary precipitation" process modifies capillary forces by altering the structure and physical properties of pore-throats, thereby influencing the microscale sweep range of CO₂ fluids. However, under identical mineral proportions, CO₂ sweep efficiency varies among samples, with higher calcite proportions correlating with broader variation in sweep performance. Conclusions These findings underscore the crucial role of physical pore properties, beyond mineral composition alone, in governing sweep efficiency. Causal learning identified key pore-throat parameters that control CO₂ microscale sweep behavior, with wettability emerging as the most influential factor. Neutrally water-wet pore-throats exhibited the highest CO₂ sweep efficiency. [Significance] By constructing a Lattice Boltzmann model for CO₂-water-rock interactions and quantifying the impact of key physical parameters, this study provides a reference and guidance for the targeted adjustment of CO₂ injection strategies and the enhancement of the geological CO₂ storage effectiveness.
- CO₂–water–rock interaction,
- lattice Boltzmann method,
- porous scale,
- causal machine learning,
- geological CO₂ sequestration

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