Machine learning in the petroleum and gas exploration phase current and future trends

Abstract

We assert that artificial intelligence is poised to significantly impact the oil and gas industry, particularly in the upstream sector, which is the most expensive and high-risk area. Our analysis of AI applications reveals clear trends in the development of powerful tools that accelerate processes and effectively reduce risks. We emphasize the importance of various AI methods and the critical need for data availability. Additionally, we address the non-technical barriers hindering AI adoption, including data-related challenges and workforce limitations. Furthermore, we outline three definitive future scenarios for the transformative role of AI in the oil and gas sector over the next 5, 10, and 20 years. Data generation in the oil and gas industry is constant and substantial, making effective recording and utilization essential. Decision-making driven by predictive and inferential data analytics empowers organizations to make swift and accurate choices. Despite existing challenges, the adoption of data analytics is on the rise and is transforming the sector. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the way complex problems are addressed and production is optimized. By leveraging both historical and real-time data from oil and gas wells, companies can significantly enhance their operational efficiency. The industry has embraced various analytical modeling techniques that facilitate decisive, data-driven strategies.

This paper decisively reviews the recent breakthroughs in AI and ML applications for data exploitation, spanning from crude oil exploration to product distribution. It also outlines the promising future of these technologies within the industry. The findings provide a clear framework for selecting the most effective technologies to manage and harness the vast data generated by the oil and gas sector.