Machine learning assisted reservoir characterization for CO2 sequestration: A case study from the Penobscot field, Canada offshore

Abstract

Rising greenhouse gas emissions, especially CO2, intensify global warming. Carbon Capture and Storage (CCS) is vital for reducing emissions from industrial sources, crucial for addressing climate change urgently. This study explores reservoir characterization challenges for CCS in the Penobscot field, offshore Nova Scotia. We used to cross-plots of geophysical logs to establish relationships between petrophysical properties and subsurface litho-facies, differentiating sand and shale facies within the Mississauga Formation (Early to Middle Cretaceous). Seismic impedance inversion distinguished between reservoir and non-reservoir facies. Multi-attribute assisted transformed seismic data and a support vector machine algorithm predicted litho-facies probabilities and effective porosity volumes in 3D space. The permutation predictor importance analysis was performed to assess the relative importance of input features in facies probabilities and effective porosity prediction. The structural component delineated the CO2 injection trap boundary. Integrated analysis of structural configuration, litho-facies probability, effective porosity, and cap-rock sealing integrity validated zones for CO2 storage. The dry, abandoned well L-30 in the structural high zone is recommended as a pilot CO2 injector well. This study provides critical insights into reservoir characterization for CO2 sequestration, aiding climate change mitigation through effective CCS, and recommends geomechanical studies and time-lapse seismic monitoring. � 2024 Elsevier Ltd