Alberta Machine Intelligence Institute

New Amii White Paper: Machine Learning in Oil & Gas

Published

Aug 15, 2020

Amii has published a new white paper outlining how oil and gas leaders can apply AI and machine learning (ML) to replace or enhance current practices.

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.

Download the White Paper

Learn how Oil & Gas organizations are using AI to enhance or replace current practices.

Authors

Hossein Shahandeh

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