Advanced Machine Learning Models for CO<sub>2</sub> and H<sub>2</sub>S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage-Reference-Cited by-同舟云学术

Advanced Machine Learning Models for CO₂ and H₂S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage

Published:2024-05-29 Issue:12 Volume:38 Page:11119-11136
ISSN:0887-0624
Container-title:Energy & Fuels
language:en
Short-container-title:Energy Fuels

Author:

Wei Wei¹^ORCID,Lu Peng²,Zhu Chen³,Luo Pan²,Mesdour Rabah⁴

Affiliation:

1. Beijing Research Center, Aramco Asia, Beijing 100102, China

2. EXPEC Advanced Research Center, Saudi Aramco, Dhahran 31311, Saudi Arabia

3. Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana 47405, United States

4. Upstream Business Support Department, Saudi Aramco, Dhahran 31311, Saudi Arabia

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.4c01423

Reference126 articles.

1. Metz, B.; Davidson, O.; de Coninck, H.; Loos, M.; Meyer, L.; IPCC. Special Report on Carbon Dioxide Capture and Storage; Cambridge University Press: UK, 2005; p 431.

2. Effects of Hydrogeological Heterogeneity on CO2 Migration and Mineral Trapping: 3D Reactive Transport Modeling of Geological CO2 Storage in the Mt. Simon Sandstone, Indiana, USA

3. Impacts of Mineral Reaction Kinetics and Regional Groundwater Flow on Long-Term CO2 Fate at Sleipner

4. Benchmark modeling of the Sleipner CO2 plume: Calibration to seismic data for the uppermost layer and model sensitivity analysis

5. Effects of rate law formulation on predicting CO2sequestration in sandstone formations

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