A Deep Learning Formation Image Log Classification Framework for Fracture Identification – A Study on Carbon Dioxide Injection Performance for the New Zealand Pohokura Field

Abstract

We have presented 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.