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A novel computational strategy to estimate CO2 solubility in brine solutions for CCUS applications

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  • Ratnakar, Ram R.
  • Chaubey, Vivek
  • Dindoruk, Birol

Abstract

Estimation of CO2 solubility in brine is crucial to various CCUS (carbon capture utilization and storage) applications, especially for engineering design of the physical/chemical processes. In this work, we developed machine learning based workflow to calculate CO2 solubility in brine at various combinations of salt mixtures, pressure, and temperatures. Most importantly, the performance of predictive models and workflow were tested against extensive experimental observations and key features of brine components with significant contributions were determined.

Suggested Citation

  • Ratnakar, Ram R. & Chaubey, Vivek & Dindoruk, Birol, 2023. "A novel computational strategy to estimate CO2 solubility in brine solutions for CCUS applications," Applied Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:appene:v:342:y:2023:i:c:s0306261923004981
    DOI: 10.1016/j.apenergy.2023.121134
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    References listed on IDEAS

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    1. Barbier, Enrico, 2002. "Geothermal energy technology and current status: an overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 6(1-2), pages 3-65.
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    1. Amin Shokrollahi & Afshin Tatar & Abbas Zeinijahromi, 2024. "Advancing CO 2 Solubility Prediction in Brine Solutions with Explainable Artificial Intelligence for Sustainable Subsurface Storage," Sustainability, MDPI, vol. 16(17), pages 1-26, August.
    2. Ligen Tang & Guosheng Ding & Shijie Song & Huimin Wang & Wuqiang Xie & Jiulong Wang, 2023. "A Case Study on the CO 2 Sequestration in Shenhua Block Reservoir: The Impacts of Injection Rates and Modes," Energies, MDPI, vol. 17(1), pages 1-19, December.
    3. He, Zhiyue & Tang, Yong & He, Youwei & Qin, Jiazheng & Hu, Shilai & Yan, Bicheng & Tang, Liangrui & Sepehrnoori, Kamy & Rui, Zhenhua, 2024. "Wellbore salt-deposition risk prediction of underground gas storage combining numerical modeling and machine learning methodology," Energy, Elsevier, vol. 305(C).

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