Machine learning is a subset of artificial intelligence (AI) that allows computer systems to learn and improve from data without being explicitly programmed. Machine learning is the most practical application of AI currently available for enterprise adoption. The Organization for Economic Co-operation and Development (OECD) estimates that AI could add as much as $16tn to the world’s GDP by 2030, equivalent to more than 10% of the gross world product.

Machine learning is a rapidly growing field in the oil and gas industry and can potentially revolutionise how companies explore and produce oil and gas. It can be used to analyse seismic data, well logs, and other geologic data to identify potential oil and gas reservoirs. Machine learning algorithms are also capable of analysing production data and identifying patterns that can be used to improve well performance. This can lead to increased production rates and reduced downtime. Besides, this analysis can also be used to identify potential hazards, thereby preventing any untoward incidents and boosting operational safety.

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The oil and gas industry has experienced two massive disruptions in just three years in the form of Covid-19 and the Ukraine conflict. While the former impacted global energy demand, the latter caused upheaval in oil and gas supply chains following the sanctions on the world’s top energy supplier Russia. This has necessitated increased oversight and performance optimisation across all functions of project design, construction, logistics, inventory management, and maintenance. Above all, companies also want better oversight into market demand to align their production The goal is to find every opportunity to lower costs to sustain in the long term.

The AI market is predicted to grow substantially from 2022 to 2026

Machine learning will benefit companies in this scenario, by driving automation, process improvement, and demand forecasting. It can support modernising maintenance practices, detecting leakages, streamlining data management and documentation, and optimising inventory and supply chains. Nevertheless, the challenge of recruiting machine learning experts with an understanding of oil and gas datasets remains a key hurdle in its adoption.

Further details of oil and gas companies and their adoption of machine learning can be found in GlobalData’s new theme report, Machine Learning in Oil and Gas.