AI/ML assisted shale gas production performance evaluation

Abstract Shale gas reservoirs are contributing a major role in overall hydrocarbon production, especially the United States, and due to intense development of such reservoirs, it is must thing learn productive methods for modeling production performance evaluation. Consequently, one most adopted techniques these days sake analysis utilization artificial intelligence (AI) machine learning (ML). Hydrocarbon exploration continuous process that brings lot data from sub-surface as well surface facilities. Availability huge set keeps on increasing over time enhances computational capabilities accuracy through AI ML applications using data-driven approach. The approach can be utilized supervised unsupervised addition neural networks (ANN). Other approaches include random forest (RF), support vector (SVM), boosting technique, clustering methods, network-based architecture, etc. In this paper, systematic literature review presented focused shale evaluation their modeling.