Enhancing CO2 Leakage Detectability via Dataset Augmentation

TitleEnhancing CO2 Leakage Detectability via Dataset Augmentation
Publication TypeConference
Year of Publication2023
AuthorsHuseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, Felix J. Herrmann
Conference NameInternational Meeting for Applied Geoscience and Energy
KeywordsCCS, classifier, deep learning, leakage detection, SEG

Previous work showed that neural classifiers can be trained to detect CO2 leakage from time-lapse seismic images. While this result is crucial to the global deployment of geological carbon storage (GCS), its success depends on relatively dense non-replicated time-lapse data acquisition. In this study, we show that by augmenting the training set with various coarse receiver samplings and corresponding seismic images, we can improve the leakage detection capabilities and accuracy while increasing the robustness with respect to low-cost coarse receiver samplings, e.g. ocean bottom nodes (OBNs).


(IMAGE, Houston)

Citation Keyerdinc2023IMAGEecl