AI/machine learning
Reaching further than dashboards and data lakes, the agentic oil field envisions artificial intelligence systems that reason, act, and optimize.
This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.
In the past year, publications on CO2, natural gas, and hydrogen storage have increasingly focused on the design, evaluation, and optimization of storage plans. These efforts encompass a broad spectrum of challenges and innovations, including the expansion of storage reservoirs from depleted gas fields and saline aquifers to stratified carbonate formations and heavy-o…
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The goal of cognitive computing is not to eliminate humans but allow highly skilled professionals to spend time doing what’s most valuable for the company. -
A look at how policy, future workforce perception, and industry standards will shape energy companies in the near and distant future.
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How can AI systems incorporate processes mimicking the slower logic- and causality-based reasoning patterns of the left brain?
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Tiny soil samples may contain as many as 300,000 species of microbial life, but a Netherlands-based startup has figured out that between 50 and 200 of them can tell an operator if a drilling location will hold oil and gas reserves.
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Southwest Research Institute is working to improve the accuracy of pipeline leak detection using sensors, artificial intelligence, and deep learning.
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As artificial intelligence makes a significant impact on various industries, an expert examines the roles it could play in streamlining oil and gas operations in the near future.
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A consultant examines the ways in which artificial intelligence and machine learning solutions may have a significant impact on industry operations.
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As the drilling industry improves its efforts to capture drilling operation activities in real time, it has generated a significant amount of data that drilling engineers cannot process on their own.
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Young Technology Showcase—Top-Down Modeling: A Shift in Building Full-Field Models for Mature FieldsData-driven, or top-down, modeling uses machine learning and data mining to develop reservoir models based on measurements, rather than solutions of governing equations.
