Introduction—an industry in upheaval
Since the successful completion of the Drake well at Titusville, Pennsylvania in 1859, the petroleum industry has undergone more than its share of ups, downs, operating/financial shocks, and technological upheavals. Today is certainly no different, with the latest performance drivers including dramatic growth in renewable power and electric vehicles, an uncertain political environment, and an increasingly wide range of sources for raw hydrocarbons driven by technology advances.
The current price of a barrel of Brent crude hovers around $63, but recent predictions from the U.S. Energy Information Administration (EIA) see this falling below $60 next year. Investment bank JP Morgan has even asserted that the price of crude could potentially fall into the 30’s by 2027 if the supply surplus continues to widen. The combination of these forces—some controllable by the industry, but many not—means that oil and gas professionals are increasingly being forced to adopt a wide range of new technologies to address the coming changes. At the very top of this technological menu is artificial intelligence (AI).
How’s it gone so far with AI adoption?
AI first began having an impact on the industry as far back as the 70s and 80s with expert systems and early neural networks. Those efforts were, however, primarily limited to prototypes, and it’s only in the years since 2010 or so that the impact of the technology has become substantial. In the decade and a half since then, AI has made very significant inroads across the full industry value chain.
Depending on the source and their details of the forecast, AI investment in the global oil and gas industry will total $4B-$5B for 2025, growing at a CAGR of 13-22% throughout the coming decade to total $10B-$33B by 2035. It’s a wide range to be sure, but no one disputes that the industry is committed to the technology and will be investing in it now—and heavily in the years to come.
AI has demonstrated value creation across the full spectrum of energy operations: upstream, downstream, and everywhere in between.
Upstream operations (drilling and exploration)
A recent McKinsey report indicates that technology-enabled exploration and drilling could generate savings of as much as $2 per barrel of oil equivalent (BOE), with a further $3 per BOE derived from AI-enabled reservoir and well management. These are significant savings, especially considering the likely commodity price reductions discussed earlier. AI contributes to upstream operations in numerous areas:
- Interpreting seismic data, analyzing drilling logs, identifying faults, and filtering noise from analytical data
- Building 3D reservoir models to measure porosity, permeability, etc.
- Reducing nonproductive time (NPT) by identifying imminent equipment failures through predictive/prescriptive maintenance
- Optimizing drilling practices and avoiding stuck-pipe incidents
- Forecasting and managing production
- Optimizing artificial lift requirements
Midstream (transportation and storage)
AI also delivers significant value across the entire value chain of midstream operations.
- Detecting pipeline anomalies and leaks
- Optimizing tanker routing
- Predicting/identifying pipeline/tank corrosion
- Performing visual/drone inspection of midstream assets, rights of way
- Optimizing flow rates and pump/compressor operations/settings
Downstream (refining and retail)
AI contributes to refinery operations, safety, and profitability, as discussed in the McKinsey report cited earlier, in which they conclude that AI-enabled hydrocarbon optimization, dynamic product blending, and sourcing optimization could net an additional $1 per BOE.
- Maximizing product yield, optimizing yield mix for greater profitability
- Minimizing energy consumption
- Ensuring output product quality
- Forecasting/mitigating equipment failures using predictive/prescriptive maintenance
- Forecasting product demand based on weather and other exogenous variables
- Optimizing product shipping schedules by truck, rail, or tanker
Case Studies
Avathon’s Autonomy Oil and Gas Operations has delivered value across many of the foregoing use cases.
—McKinsey & Company
- European Supermajor—With applications deployed across numerous platforms and refineries, a large European oil and gas company implemented the Avathon Autonomy platform with the goal of leveraging the technology across its entire business. The company realized a 98.7% reduction in the number of alerts and achieved 75-day advance notice of impending equipment failures, resulting in $30M savings.
- Middle Eastern Supermajor—A Middle Eastern supermajor used Avathon Autonomy to optimize its daily maritime shipments of 4M barrels, or 50% of capacity. Their implementation of Avathon’s platform applied knowledge representation and analytics to the company’s operating data to optimize routing of petroleum shipments using computational knowledge graph technology, dynamic schedule optimization, and scenario planning. This enabled the company to more efficiently ship over 2B barrels, resulting in a 98% labor cost reduction, saving $64M.
- Large European Energy Provider—This company analyzed time-series data to predict the performance of its large turbines, increasing reliability and uptime. The Autonomy platform captured 75% of compressor events with eight days of advance notice and also reduced the rate of false positives. This resulted in a 5.1% increase in annual operating efficiency and a $600K reduction in annual maintenance expense following initial implementation.
Conclusion
The oil and gas industry faces profound challenges—and opportunities—in the coming years due not only to the rapid growth in renewable energy and EVs, but also the accelerating pace of technology in everything from exploration and production to transportation to refinery operations. AI can create immense value in the various operating areas unique to each major energy sector as described above, but there is also significant opportunity for value creation across the full oil and gas value chain in critical areas like logistics, supply chain management, environmental compliance, and worker safety.
Avathon’s Autonomy Platform for Oil and Gas Operations leverages the vast quantities of operating data that upstream, midstream, and downstream operators possess and turns that information into valuable insights that maximize asset performance, production outputs, and worker safety, all using the power of AI.
To learn more about Avathon Autonomy for Oil and Gas Operations solutions, visit our website or read our white paper.
