As discussed in a previous blog, AI is expected to drive growth in the global economy and the oil and gas industry is no exception. AI is being implemented across the oil and gas value chain and finding uses in everything from exploration to refining.
In exploration, AI and machine learning (ML) tools are being used to process and analyze subsurface seismic data. Seismic exploration is time consuming because of the vast amounts of data involved. AI and ML tools allow geophysicists to do their work in a fraction of the time by accelerating the processing, interpretation, and management of the data. This leads to a significant reduction in cost and time for discovering new wells. Although a number of upstream software tools now claim to be AI-enhanced, ADI’s research has shown that only a few players (e.g., Bluware’s InteractiveAI) are true leaders in this space.
AI algorithms are being used maximize the production of wells with advanced monitoring and automation to enhance operations. Flow rate and pressure are just some of the key variables in a well’s operation. By monitoring and adjusting these key variables in real-time, AI and ML tools allow operators to maximize production and reduce downtime.
Brownfield assets are also benefiting from improved retrieval and analysis of data using AI. Tools using 4D seismic technology are being employed to improve the quality of wells. By optimizing drainage, recovery is maximized, leading to more profitable brownfield assets.
Exhibit 1. Examples of companies utilizing AI in oil and gas.
Asset management and monitoring is another application the oil and gas industry is finding for AI. Digital twins are one of the technologies gaining momentum recently. Digital twins effectively serve as indistinguishable digital counterparts to real-world or planned assets. These models aid in managing the array of costly assets used in oil and gas, from drilling rigs to refineries. Facilitating predictive maintenance by monitoring critical components in real-time digital twins can be used find defects before they occur and optimize maintenance schedules to reduce downtime.
AI and ML is also being used to reduce the carbon footprint of oil and gas through improved emissions detection and monitoring. Imaging technology is being used to identify methane plumes and quantify emissions. With this and improved sensors and geolocation, fugitive emissions can be easily identified and remedied. Oil majors such as Chevron have started leveraging satellites along with ML to detect and prevent emissions through timely repairs.
Refineries are also likely to benefit from AI and ML. Digital twins and emissions detection will be beneficial for refineries, but the greatest impact will be the ability to quickly optimize crack spreads. AI will allow refineries to rapidly adjust to changing market conditions and maximize margins between input feedstocks and output products.
If you enjoyed reading about how the oil and gas industry is primed to reap the benefits of AI technologies, keep an eye out for upcoming blogs discussing how chemicals, utilities, industrials, and mining are all benefiting from generative AI, ML, and other developments in advanced computing.
– Piercen Hoekstra
ADI Analytics is a prestigious, boutique consulting firm specializing in oil & gas, energy transition, and chemicals since 2009. We bring deep, first-rate expertise in a broad range of markets including oil & gas, chemicals, and utilities, where we support Fortune 500, mid-sized and early-stage companies, and investors with consulting services, research reports, and data and analytics, with the goal of delivering actionable outcomes to help our clients achieve tangible results.
We also host the ADI Forum, one of Houston’s distinguished industry conferences, to bring c-suite executives from oil & gas, energy transition, and chemicals together for meaningful dialogue and strategic insights across the value chains.
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