Abstract
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.
Highlights
Shale gas production was estimated to be at 50% of the net total of natural gas production as of 2018 in the USA, which eventually has gone even higher as of today
The weight and bias are updated regularly through optimization algorithms which works on minimizing the cost or loss functions such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and others to reach at an optimized value
This paper presents a systematic review covering machine learning approaches in shale gas production performance evaluation and modeling
Summary
Shale gas production was estimated to be at 50% of the net total of natural gas production as of 2018 in the USA, which eventually has gone even higher as of today. Conventional reservoir engineering approaches including the experimental, analytical and numerical modeling do come handy in solving different challenges of shale gas reservoir which involves its characterization, production forecasting, and resource management (Syed, F.I., et al 2021). Random forest (RF) is an ML approach used in producing predictions by the construction of more than one decision tree where every single tree consisting of different learning data set. This method ensembles all trees including the regression and classification trees based on specific type of problem presented.
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