Data & Analytics
This article from the SPE Robotics and Autonomous Systems Technical Section (RASTS) explores the insights shared at the recent Offshore Technology Conference (OTC) in Houston about autonomous systems and their role in the industry's future.
The technology has passed its first phase of qualification, with 84 nodes placed on the seafloor at a depth of 2,000 m to acquire 4D seismic data in the pre-salt Santos Basin.
The company says its curated service aims to connect energy professionals, developers, and partners to discover, deploy, and scale trusted AI agents, domain models, and digital applications.
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Artificial intelligence is prompting oil and gas companies to redefine roles, rethink trust, and rework operations, experts said during CERAWeek. -
The gap between machine learning research and effective deployment in the oil and gas industry is an alignment challenge between research questions and real decisions, between model design and operational constraints, and between innovation and the people expected to use it.
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Technology and partnerships remain important, while phased approaches may supplant lengthy appraisal programs, experts said during CERAWeek.
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CERAWeek panelists see AI as a way to leverage data and people in interpreting data for exploration, but a cultural shift at companies may still be needed.
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Data centers could add up to 6 Bcf/D of US gas demand by 2030, creating a new opportunity for producers and reshaping how oil companies think about electricity supply.
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Two examples from ONGC show how supervised AI-driven automation scaled well modeling across hundreds of offshore wells, saving more than 1,000 engineering hours.
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Examples demonstrate how an Integrated Operations Center as a Service (IOCaaS) model, powered by artificial intelligence, reduced costs by 5% and increased production by 6% in Canada.
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This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.
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The authors propose a deep-learning-based approach enabling near-real-time CO2-plume visualization and rapid data assimilation incorporating multiple geological realizations for predicting future CO2 plume evolution and area-of-review determination.
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This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.
