Artificial intelligence approach to total organic carbon content prediction in shale gas reservoir using well logs: A review

Abstract

The most important element for the exploration and development of oil and oil shale is total organic carbon (TOC). TOC estimation is considered a challenge for geologists since laboratory methods are expensive and time-consuming. Therefore, due to the complex and nonlinear relationship between well logs and TOC, researchers have begun to use artificial intelligence (AI) techniques. Hence, the purpose of this research is to explore new paradigms and methods for AI techniques. First, this article provides a recent overview of selected AI technologies and their applications, including artificial neural networks (ANNs), convolutional neural networks (CNNs), hybrid intelligent systems (HISs), and support vector machines (SVMs) as well as fuzzy logic (FL), particle swarm optimization (PSO). Second, this article explores and discusses the benefits and pitfalls of each type of AI technology. The study found that hybrid intelligence technology was the most successful and independent AI model with the highest probability of infer-ring properties of oil shale oil and gas fields (such as TOC) from wireline logs. Finally, some possible combinations are proposed that have not yet been investigated.