Smart shale gas production performance analysis using machine learning applications

Abstract

With the advancement of technology and innovation in the oil and gas industry, the production of liquid and gaseous hydrocarbon from conventional and unconventional resources has seen exponential growth. Recently, the USA and other oil giants have shifted their paradigm from conventional to unconventional resources of exploration and production of hydrocarbon. However, there is still a perpetual force that exists to develop and devise new innovative approaches and methodologies for the exploration, extraction efficiencies, and production performance of hydrocarbons. To better evaluate the impact of well attributes, reservoir characteristics and production behavior of well machine learning and artificial intelligence-based models have been developed by researchers that with the help of simulation and modeling provide us the true picture of reservoir performance without exploring and investing billions of dollars. This review paper encompasses the literature published in the recent years and narrated the recent development made by researchers especially in the field of production performance estimation of shale gas by developing machine learning-based models. More specifically, this paper deals with the major shale gas reservoir of North America including Marcellus shale, Eagle Ford shale, and Bakken Shale. Additionally, equations, input parameters, and formations that are considered key parameters for the development of the smart shale gas models are also discussed in this manuscript. In addition, the methodology comparison of different machine learning algorithms including their limitations and advantages are also presented.