Application of Machine Learning Algorithms to Predict Gas Sorption Capacity in Heterogeneous Porous Material

Tasbiha Ibad, Syed Muhammad Ibad (), Haylay Tsegab and Rabeea Jaffari
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Tasbiha Ibad: Computer Information Science Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Syed Muhammad Ibad: Department of Petroleum Geoscience, Faculty of Geoscience & Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Haylay Tsegab: Southeast Asia Clastic and Carbonate Research Laboratory, Department of Geoscience, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Rabeea Jaffari: Software Engineering Department, Mehran University of Engineering and Technology, Sindh, Jamshoro 76062, Pakistan

Resources, 2025, vol. 14, issue 5, 1-22

Abstract: Shale gas is a clean and effective energy source that plays a big part in the transition from high-carbon to low-carbon energy, serving as a link for the growth of low-carbon energy in the future. Since shale rock is a heterogeneous porous material, the best production strategy is determined by a precise assessment of geological gas-in-place. Therefore, the economic and technical foresight of the production operations depends on the estimation of the adsorbed gas amount in shale resources. The isotherm curves of shale gas derived in this study were classified as type 1 isotherms, which indicates the presence of micropores in these samples. In this work, XGBoost (extreme gradient boosting) and ANN (artificial neural network) optimized with ABC (artificial bee colony) and PSO (particle swarm optimization) have been proposed to learn and then predict the methane sorption capacity (MSC) in shale based on total organic carbon (TOC), temperature, pressure, and moisture as input variables, with the gas adsorption amount of shale as the output. Statistical and graphical methods were used to compare the experimental results with the expected values. By comparison, the current work’s ANN-ABC and ANN-PSO models outperform all previous studies with higher R 2 values (0.9913 and 0.9954) and lower RMSE scores (0.0457 and 0.0420), respectively, indicating improved predictive accuracy and generalization ability. The findings demonstrate that, in comparison to earlier models, the suggested models provide an exceptional prediction of the adsorbed gas amount in a heterogeneous porous medium. With additional data available, it may be easily updated for wider applications. Overall, this paper shows that machine learning can be used to forecast shale gas adsorption, and a well-trained model may be incorporated into a large numerical framework to optimize shale gas production curves.

Keywords: clean energy; heterogeneous porous material; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2025
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