Smart detection of fractures in formation image logs for enhanced CO2storage

Abstract

Carbon capture and storage (CCS) has attracted strong interest from industry and the scientific community alike due to the ability of storing CO2 in subsurface reservoirs. Deep saline aquifers may be well suited for the safe and long-term storage given their geological structure. The long term underground storage in saline aquifers depends on variety of interrelated trapping mechanisms in addition to the caprock sealing efficiency. Fractures are commonplace in many geological settings and represent a crucial role for hydrocarbon migrations and entrapment. Fracture impact fluid flow in variety of forms, particularly due to the complexity and varying natures of the fractures, which channel the injected CO2 throughout the reservoir formation. This is especially important for tight gas reservoirs and low permeable cap rock structures whose permeability is primarily characterized by the fault and fractures. This outlines the importance of determining accurately fracture penetration in wellbores for CO2 injection. We present a new deep learning framework for the detection of fractures in formation image logs for enhancing CO2 storage. Fractures may represent high velocity gas flow channels which may make CO2 storage a challenge. The novel deep learning framework incorporates both acoustic and electrical formation image logs for the detection of fractures in wellbores for CO2 storage enhancement and injection optimization. The framework was evaluated on the Pohokura-1 well for the detection of fractures, with the framework exhibiting strong classification accuracy. The framework could accurately classify the fractures based on acoustic and electrical image logs in 98.1% for the training and 85.6% for the testing dataset. Furthermore, estimates of the fracture size are strong, indicating the ability of the framework to accurately quantify fracture sizes in order to optimize CO2 injection and storage.