Python driven pore network modelling for evaluating H2 storage in geological formations through capillary pressure and relative permeability curves
Author Identifier
Shubham Saraf: https://orcid.org/0000-0002-3910-147X
Alireza Keshavarz: https://orcid.org/0000-0002-8091-961X
Stefan Iglauer: https://orcid.org/0000-0002-8080-1590
Document Type
Conference Proceeding
Publication Title
APOGCE 2024 - SPE Asia Pacific Oil and Gas Conference and Exhibition
Publisher
Society of Petroleum Engineers
School
School of Engineering
Funders
King Abdullah University of Science and Technology
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.
DOI
10.2118/221099-MS
Access Rights
subscription content
Comments
Saraf, S., Keshavarz, A., Ali, M., & Iglauer, S. (2024, October). Python driven pore network modelling for evaluating H2 storage in geological formations through capillary pressure and relative permeability curves. APOGCE 2024: Perth, Australia. https://doi.org/10.2118/221099-MS