№2, 2020


Ramiz H. Shikhaliyev

The extraction, refining and delivery of oil and gas products is expensive. Therefore, the main tasks of the oil and gas industry that need to be addressed are to increase the productivity of oil and gas production and minimize the cost of processing and delivery of the products to end consumers. When solving these tasks, many problems arise, such as the problems of oil and gas exploration and production, detection of anomalies in the operation of drilling rigs, detection of infrastructure risks in oil pipelines, prediction of well characteristics, minimization of expenses on oil and gas production and transportation, and detection of leaks during oil transportation and gas pipelines, risk assessment and management, forecasting oil price volatility, etc. The solution to most problems by traditional methods of data analysis is not possible, since the processes of the oil and gas industry are non-deterministic due to their non-linear nature, and also these processes generate very large amounts of data. Therefore, in the last decade, to solve the problems of the oil and gas industry, the methods based on artificial intelligence, in particular, on machine learning (ML) methods, have been proposed in the literature. This article provides a review of the literature on the application of ML methods to solve various problems of the oil and gas industry, which allow to determine the potential of ML methods and more widely implement them in the oil and gas industry (pp.52-60).

Keywords: oil and gas industry, artificial intelligence, machine learning methods, support vector method, artificial neural networks, deep learning, genetic algorithms.
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