Machine Learning in Oil & Gas


Executive Summary

More recently, the demand for oil & gas has been significantly disrupted by the global health crisis caused by the COVID-19 pandemic. The lockdown efforts and travel restrictions adopted by many countries have curtailed energy demand worldwide.

The International Energy Agency projects a fall of six percent across all products, potentially making it the largest decline in history — seven times larger than the impact of the 2008 financial crisis. While energy demand is beginning to rebound with the gradual reopening of the economy, oil & gas producers are viewing this as an opportunity to rethink processes, find efficiencies and ultimately become more resilient to the cyclical nature of the energy industry.

Machine learning (ML) — a set of computational techniques that uses data to predict future outcomes — has an important role to play in this overhaul. Using a combination of ML and data analytics, oil & gas leaders can extract valuable insights from the volumes of available data collected by various capital assets. These insights can apply to nearly all aspects of the business — from capital expenditures to risk mitigation, and real-time monitoring and analysis. The adoption of ML can help oil & gas companies lower maintenance costs, reduce unplanned downtime, improve safety outcomes and inform opportunities for system improvements — leading to better business and operational decisions.

In a world of lower oil prices, producers need to adopt every measure that makes them more competitive and sustainable. In addition to near- and mid-term economic and societal pressures, the decreasing cost of technology, ever-widening connectivity of devices and exponential increases in computational power make it an opportune time for oil & gas companies to invest in ML. However, adopting new technological practices can be a daunting task. Many oil & gas leaders do not know where to start.

This paper outlines the various ways ML can be applied to replace or enhance current practices. It will also demonstrate how oil & gas companies can use sets of historical and real-time data to solve business problems and allow managers and executives to gauge whether they are ready to implement ML now or in the near future.

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