Maximizing CO2 injectivity within fracture pressure

TitleMaximizing CO2 injectivity within fracture pressure
Publication TypePresentation
Year of Publication2023
AuthorsHaoyun Li, Ziyi Yin, Olav M√łyner, Felix J. Herrmann
KeywordsCCS, ML4SEISMIC, Optimization, reservoir simulation, SLIM
Abstract

In geological carbon storage projects, optimizing CO2 injection strategies is paramount to enhance storage efficiency and prevent leakage. The objective is to maximize the CO2 injection volume without surpassing the fracture pressure. Traditional adjoint-based approaches necessitate extensive numerical simulations, leading to significant computational overhead. To circumvent this challenge, we introduce an optimization framework based on physics-informed deep convolutional neural networks. Trained on different permeability slices, our model can rapidly predict the maximal CO2 injection volume for new permeability fields in real-time.

URLhttps://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/li2023ML4SEISMICmci
Citation Keyli2023ML4SEISMICmci