Artificial intelligent in upstream oil and gas industry: a review of applications, challenges and perspectives

Abstract

In the last two decades, oil and gas (O&G) industries are facing several challenges and issues in different levels; from the decrease in commodity prices to the dynamic and unexpected environment. There has been a constant urge to maximize benefits and attain values from limited resources. Traditional empirical and numerical simulation techniques have failed to provide comprehensive optimized solutions in little time due to the Immense amount of data generated on daily basis with various formats, techniques and process. The proper technical analysis of this “explosion of data” is to be carried out to improve performance of O&G industries. Artificial intelligence (AI) has found extensive usage in simplifying complex decision-making procedures in practically every competitive market field, and O&G industry is not an exception. This paper provides a comprehensive stateof- art review in the field of machine learning and artificial intelligence to solve O&G industry problems. We focus on the upstream segment as the most capital- intensive part of oil and gas and the segment of enormous uncertainties to tackle. Based on a summary of various researchers work on machine learning and AI applications, we outline the most recent trends in developing AI-based tools and identify their effects on accelerating the process in the industry. This paper discusses also the main challenges related to non-technical factors that prevent the intensive application of AI in the upstream O&G industry.