Python Driven Pore Network Modelling for Evaluating H2 Storage in Geological Formations Through Capillary Pressure and Relative Permeability Curves

Abstract This study utilizes micro-CT scan data to construct accurate pore network models for predicting carbon and hydrogen storage in geological formations by pore scale modeling. The creation of a dynamic pore network model utilizes advanced Python techniques, enabling the effective machine investigation of petrophysical data. This model is utilized to assess the capacity for storing substances, specifically examining how these gases are distributed and their ability to be trapped inside the formations. An innovative Python technique is used on an adaptable sandstone three-dimension pore network model, which optimizes computational processes for analyzing correlations between capillary pressure-saturation. NumPy, SciPy, and NetworkX facilitate the creation of a reliable pore network model with modern features. The model evaluates the efficiency of hydrogen and carbon storage, offering detailed understanding of characteristics. The simulations were executed utilising the poreFoam a data analyst, a Python-based tool developed on the foamExtend update, which is a modified version obtained from OpenPNM/OpenFoam. The study emphasizes important areas where sequestration occurs and emphasizes the importance of using modern computational tools to accurately assess storage, which improves the comprehension of geological sequestration.