Semester
Fall
Date of Graduation
2023
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Petroleum and Natural Gas Engineering
Committee Chair
Shahab D. Mohaghegh
Committee Member
Samuel Ameri
Committee Member
Kashy Aminian
Abstract
This research builds upon the success of a previous project that used a Smart Proxy Model (SPM) to predict pressure and saturation in Carbon Capture and Storage (CCS) operations into saline aquifers. The Smart Proxy Model is a data-driven machine learning model that can replicate the output of a sophisticated numerical simulation model for each time step in a short amount of time, using Artificial Intelligence (AI) and large volumes of subsurface data. This study aims to develop the Smart Proxy Model further by incorporating geomechanical datadriven techniques to predict shear stress by using a neural network, specifically through supervised learning, to construct Smart Proxy Models, which are critical to ensuring the safety and effectiveness of Carbon Capture and Storage operations. By training the Smart Proxy Model with reservoir simulations that incorporate varying geological properties and geomechanical data, we will be able to predict the distribution of shear stress. The ability to accurately predict shear stress is crucial to mitigating the potential risks associated with Carbon Capture and Storage operations. The development of a geomechanical Smart Proxy Model will enable more efficient and reliable subsurface modeling decisions in Carbon Capture and Storage operations, ultimately contributing to the safe and effective storage of CO2 and the global effort to combat climate change.
Recommended Citation
Alawadh, Munirah, "Leveraging Artificial Intelligence and Geomechanical Data for Accurate Shear Stress Prediction in CO2 Sequestration within Saline Aquifers (Smart Proxy Modeling)" (2023). Graduate Theses, Dissertations, and Problem Reports. 12191.
https://researchrepository.wvu.edu/etd/12191