Review of application of artificial intelligence techniques in petroleum operations

Abstract

In the last few years, the use of artificial intelligence (AI) and machine learning (ML) techniques have received considerable notice as trending technologies in the petroleum industry. The utilization of new tools and modern technologies creates huge volumes of structured and un-structured data. Organizing and processing of these information at faster pace for the performance assessment and forecasting for field development and management is continuously growing as an important field of investigation. Various difficulties which were faced in predicting the operative features by utilizing the conventional methods have directed the academia and industry toward investigations focusing on the applications of ML and data driven approaches in exploration and production operations to achieve more accurate predictions which improves decision-making processes. This research provides a review to examine the use cases and application of AI and ML techniques in petroleum industry for optimization of the upstream processes such as reservoir studies, drilling and production engineering. The challenges related to routine approaches for prognosis of operative parameters have been evaluated and the use cases of performance optimizations through employing data-driven approaches resulted in enhancement of decision-making workflows have been presented. Moreover, possible scenarios of the way that artificial intelligence will develop and influence the oil and gas industry and how it may change it in the future was discussed.

Keywords

• Artificial intelligence
• Machine learning
• Upstream operation
• Oil and gas industry
• Petroleum systems
• Decision-making