期刊
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
卷 11, 期 9, 页码 3509-3519出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13202-021-01253-8
关键词
Shale gas; ANN; Machine learning; SVM; Boosting technique; Clustering
This paper introduces the application of AI and ML in shale gas production performance evaluation, highlighting the enhancement of performance accuracy through data-driven methods, and discusses the utilization of various ML methods.
Shale gas reservoirs are contributing a major role in overall hydrocarbon production, especially in the United States, and due to the intense development of such reservoirs, it is a must thing to learn the productive methods for modeling production and performance evaluation. Consequently, one of the most adopted techniques these days for the sake of production performance analysis is the utilization of artificial intelligence (AI) and machine learning (ML). Hydrocarbon exploration and production is a continuous process that brings a lot of data from sub-surface as well as from the surface facilities. Availability of such a huge data set that keeps on increasing over time enhances the computational capabilities and performance accuracy through AI and ML applications using a data-driven approach. The ML approach can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN). Other ML approaches include random forest (RF), support vector machine (SVM), boosting technique, clustering methods, and artificial network-based architecture, etc. In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling.
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