Data-driven modeling to predict adsorption of hydrogen on shale kerogen: Implication for underground hydrogen storage: International Journal of Coal Geology

S. Kalam, M. Arif, A. Raza, N. Lashari, M. Mahmoud

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The interaction of hydrogen in shale gas formations holds significant interest for long-term subsurface hydrogen storage. Accurately and rapidly predicting hydrogen adsorption in these formations is crucial for assessing underground hydrogen storage potential. Many laboratory experiments and molecular simulations have been conducted to determine hydrogen adsorption. However, laboratory experiments and molecular simulations require complex setups and extensive calculations, which can be time-consuming. Consequently, end-users may prefer quick and accurate prediction of hydrogen adsorption to reduce the experimental and computational burden. This study introduces a novel model for predicting hydrogen adsorption using gradient boosting regression and available molecular simulation data from the literature. The data-driven model predicts hydrogen adsorption on kerogen structures based on pressure, temperature, adsorbed methane, hydrogen-to‑carbon ratio, oxygen-to‑carbon ratio, and kerogen density. We compared gradient-boosting regression with other machine learning tools, including artificial neural networks, symbolic regression assisted with genetic programming, decision trees, and random forests in terms of their capability to predict H2 adsorption on shale kerogen. A simple mathematical equation based on symbolic regression via genetic programming has also been provided, with training and testing coefficients of determination of 88.4% and 85.8%, respectively. However, the digital model created using gradient boosting regression outperformed all other machine learning tools, achieving a coefficient of determination of 99.6% for training data and 94.6% for testing data. A sensitivity analysis was also conducted that demonstrates the robustness of the developed model. In the case of kerogen type A, the order of increasing hydrogen adsorption is KIA < KIIA
Original languageBritish English
JournalInt. J. Coal Geol.
Volume280
DOIs
StatePublished - 2023

Keywords

  • Data-driven modeling
  • Hydrogen adsorption
  • Kerogen
  • Machine learning
  • Shale gas reservoirs
  • Underground hydrogen storage
  • Adaptive boosting
  • Carbon
  • Decision trees
  • Digital storage
  • Forecasting
  • Gas adsorption
  • Genetic algorithms
  • Hydrogen storage
  • Molecular structure
  • Neural networks
  • Regression analysis
  • Sensitivity analysis
  • Shale gas
  • Data-driven model
  • Gas formation
  • Gradient boosting
  • Laboratory experiments
  • Learning tool
  • Machine-learning
  • Molecular simulations
  • adsorption
  • hydrocarbon reservoir
  • hydrogen
  • kerogen
  • machine learning
  • modeling
  • shale gas
  • underground storage

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