Artificial Intelligence Approach for Predicting TOC From Well Logs in Shale Reservoirs

Abstract

Total organic carbon (TOC) is the most significant factor for shale oil and gas exploration and development which can be used to evaluate the hydrocarbon generation potential of source rock. However, estimating TOC is a challenge for the geological engineers because direct measurements of core analysis geochemical experiments are time-consuming and costly. Therefore, many AI technique has used for TOC content prediction in the shale reservoir where AI techniques have impacted positively. Having both strength and weakness, some of them can execute quickly and handle high dimensional data while others have limitation for handling the uncertainty, learning difficulties, and unable to deal with high or low dimensional datasets which reminds the “no free lunch” theorem where it has been proven that no technique or system be relevant to all issues in all circumstances. So, investigating the cutting-edge AI techniques is the contribution of this study as the resulting analysis gives top to bottom understanding of the different TOC content prediction strategies.