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  1. A Machine Learning Based Framework for Brine-Gas Interfacial Tension Prediction: Implications for H2, CH4 and CO2 Geo-Storage
A Machine Learning Based Framework for Brine-Gas Interfacial Tension Prediction: Implications for H2, CH4 and CO2 Geo-Storage
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A Machine Learning Based Framework for Brine-Gas Interfacial Tension Prediction: Implications for H2, CH4 and CO2 Geo-Storage

Pan, B

A Machine Learning Based Framework for Brine-Gas Interfacial Tension Prediction: Implications for H2, CH4 and CO2 Geo-Storage

Pan, B ; Sharif University of Technology | 2024

112 Viewed
  1. Type of Document: Article
  2. DOI: 10.2118/219225-MS
  3. Publisher: 2024
  4. Abstract:
  5. Brine-gas interfacial tension (γ) is an important parameter to determine fluid dynamics, trapping and distributions at pore-scale, thus influencing gas (H2, CH4 and CO2) geo-storage (GGS) capacity and security at reservoir-scale. However, γ is a complex function of pressure, temperature, ionic strength, gas type and mole fraction, thus time-consuming to measure experimentally and challenging to predict theoretically. Therefore herein, a genetic algorithm-based automatic machine learning and symbolic regression (GA-AutoML-SR) framework was developed to predict γ systematically under GGS conditions. In addition, the sensitivity of γ to all influencing factors was analyzed. The prediction results have shown that: 1. the GA-AutoML-SR model prediction accuracy was high with the coefficient of determination (R2) of 0.994 and 0.978 for the training and testing sets, respectively; 2. a quantitative mathematical correlation was derived as a function of pressure, temperature, ionic strength, gas type and mole fraction, with R2= 0.72; 3. the most dominant influencing factor for γ was identified as pressure. These insights will promote the energy transition, balance energy supply-demand and reduce carbon emissions. Copyright © 2024, Society of Petroleum Engineers
  6. Keywords:
  7. CH4 ; CO2 geo-storage ; H2 ; Interfacial tension ; Machine learning ; Forecasting ; Gases ; Genetic algorithms ; Ionic strength ; Automatic machines ; CH 4 ; CO2 geo-storage ; Fluid distribution ; Function of pressure ; Gas type ; H2 ; Molefraction ; Symbolic regression ; Carbon dioxide
  8. Source: Society of Petroleum Engineers - GOTECH Conference 2024 ; 2024 ; 978-195902540-5 (ISBN)
  9. URL: https://onepetro.org/SPEGOTS/proceedings-abstract/24GOTS/24GOTS/D022S001R005/545183