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Physics-Based Proxy Modeling of CO 2 Sequestration in Deep Saline Aquifers

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  • Aaditya Khanal

    (Jasper Department of Chemical Engineering, University of Texas at Tyler, Tyler, TX 75799, USA)

  • Md Fahim Shahriar

    (Jasper Department of Chemical Engineering, University of Texas at Tyler, Tyler, TX 75799, USA)

Abstract

The geological sequestration of CO 2 in deep saline aquifers is one of the most effective strategies to reduce greenhouse emissions from the stationary point sources of CO 2 . However, it is a complex task to quantify the storage capacity of an aquifer as it is a function of various geological characteristics and operational decisions. This study applies physics-based proxy modeling by using multiple machine learning (ML) models to predict the CO 2 trapping scenarios in a deep saline aquifer. A compositional reservoir simulator was used to develop a base case proxy model to simulate the CO 2 trapping mechanisms (i.e., residual, solubility, and mineral trapping) for 275 years following a 25-year CO 2 injection period in a deep saline aquifer. An expansive dataset comprising 19,800 data points was generated by varying several key geological and decision parameters to simulate multiple iterations of the base case model. The dataset was used to develop, train, and validate four robust ML models—multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). We analyzed the sequestered CO 2 using the ML models by residual, solubility, and mineral trapping mechanisms. Based on the statistical accuracy results, with a coefficient of determination (R 2 ) value of over 0.999, both RF and XGB had an excellent predictive ability for the cross-validated dataset. The proposed XGB model has the best CO 2 trapping performance prediction with R 2 values of 0.99988, 0.99968, and 0.99985 for residual trapping, mineralized trapping, and dissolution trapping mechanisms, respectively. Furthermore, a feature importance analysis for the RF algorithm identified reservoir monitoring time as the most critical feature dictating changes in CO 2 trapping performance, while relative permeability hysteresis, permeability, and porosity of the reservoir were some of the key geological parameters. For XGB, however, the importance of uncertain geologic parameters varied based on different trapping mechanisms. The findings from this study show that the physics-based smart proxy models can be used as a robust predictive tool to estimate the sequestration of CO 2 in deep saline aquifers with similar reservoir characteristics.

Suggested Citation

  • Aaditya Khanal & Md Fahim Shahriar, 2022. "Physics-Based Proxy Modeling of CO 2 Sequestration in Deep Saline Aquifers," Energies, MDPI, vol. 15(12), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4350-:d:838785
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    References listed on IDEAS

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    1. Vo Thanh, Hung & Lee, Kang-Kun, 2022. "Application of machine learning to predict CO2 trapping performance in deep saline aquifers," Energy, Elsevier, vol. 239(PE).
    2. Rashid Mohamed Mkemai & Gong Bin, 2020. "A modeling and numerical simulation study of enhanced CO2 sequestration into deep saline formation: a strategy towards climate change mitigation," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(5), pages 901-927, May.
    3. Kim, Youngmin & Jang, Hochang & Kim, Junggyun & Lee, Jeonghwan, 2017. "Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network," Applied Energy, Elsevier, vol. 185(P1), pages 916-928.
    4. Jiang, Xi, 2011. "A review of physical modelling and numerical simulation of long-term geological storage of CO2," Applied Energy, Elsevier, vol. 88(11), pages 3557-3566.
    5. Chen, Bailian & Pawar, Rajesh J., 2019. "Characterization of CO2 storage and enhanced oil recovery in residual oil zones," Energy, Elsevier, vol. 183(C), pages 291-304.
    6. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
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    1. Aaditya Khanal & Md Fahim Shahriar, 2023. "Optimization of CO 2 Huff-n-Puff in Unconventional Reservoirs with a Focus on Pore Confinement Effects, Fluid Types, and Completion Parameters," Energies, MDPI, vol. 16(5), pages 1-23, February.

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