Mahdieh Zakizadeh - Department of Computer Engineering, South Tehran Branch, Islamic AzadUniversity, Tehran, Iran
Mozhdeh Tanha - Phd, Department of Computer Engineering, Islamic Azad, University, Iran

The oil and gas industries are increasingly embracing machinelearning (ML) techniques to enhance various aspects of their operations,ranging from exploration and production to refining and distribution. Thiscomprehensive literature review aims to provide a holistic understanding ofthe current state of ML applications in the oil and gas industries. Bycritically analyzing a wide range of academic and industry publications, thismanuscript highlights the strengths and limitations of different MLalgorithms deployed in this sector. The review commences with anoverview of ML concepts and relevant algorithms commonly utilized in theoil and gas context. Next, various applications of ML in different phases ofexploration, production, refining, and distribution are explored. Theseapplications include the prediction of reservoir properties, optimization ofdrilling operations, anomaly detection in pipeline systems, and demandforecasting. Additionally, challenges such as data availability, qualityissues, and interpretability of ML models specific to the oil and gasindustries are discussed. The findings of this review shed light on thesuccesses achieved thus far and provide insights into areas where furtherresearch and development are needed. The manuscript concludes byoutlining potential future directions for the utilization of ML in the oil andgas industries, including the integration of ML with other emergingtechnologies.

Machine Learning, Oil & Gas Industries, Prediction, Dataanalysing