Machine Learning Techniques for Natural Gas Exploration, Production and Distribution: A Review

Abstract

Machine learning (ML) technology is increasingly becoming an important tool in the natural gas industry, providing advancements in areas such as exploration, production, reservoir characterization, and pipeline transportation. Unfortunately, the classical methods have certain shortcomings, which include the employment of complicated non-linear models and high computational cost as well as large-scale reservoir simulation problem. To address the aforementioned challenges, various ML models, including but not limited to support vector machines (SVMs), random forests (RF), linear regression (LR), extreme gradient boosting (XGBoost), artificial neural networks (ANNs), and other types of deep learning techniques, have been applied in forecasting, prediction, fault diagnosis, and optimization applications. The review paper will give an insight into the application of machine learning techniques in natural gas exploration and production process and pipeline transport. It will highlight the capabilities of ML techniques to analyze and deal with big data, improve prediction accuracy, facilitate intelligence-based decision making and increase operational efficiency in the natural gas industry. Furthermore, it will discuss some limitations of ML approaches in the field. Finally, future research directions are presented highlighting the need for ML in creating intelligent, reliable, and automated natural gas systems for sustainable industrial operations.