AI/machine learning
Chevron and Halliburton describe how they built and deployed the fully autonomous closed-loop fracturing system that enables subsurface-driven optimization.
Aramco says it has saved $770 million over the past 3 years from the $70 million it has invested over the same period in corrosion management technologies.
The deal adds physics-based reservoir modeling and real-time decision workflows to SLB’s digital portfolio.
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Accenture research suggests that only 12% of companies have advanced their AI maturity enough to achieve superior growth and business transformation. These companies are "AI Achievers" and, on average, attribute 30% of their total revenue to AI.
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Their model’s predictions should help researchers improve ocean climate simulations and hone the design of offshore structures.
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The paper describes the experience of using a machine-learning model prepared by the ensemble method to prevent stuck-pipe events during well construction in extended-reach wells.
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This paper presents the development and test of a method to predict upstream events that could lead to flaring, applying an integrated framework using machine-learning and big-data analytics.
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A field test conducted by Yokogawa Electric and JSR resulted in a chemical plant being run autonomously for the first time by artificial intelligence for 35 days.
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C3 AI reported that the oil major has hit the milestone of 10,000 pieces of equipment being monitored by its predictive-maintenance artificial intelligence.
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This paper describes a novel machine-learning approach for processing distributed fiber-optic sensing data that enables dynamic flow-profile monitoring using a fiber-optic electric-line cable deployed in a gas condensate well.
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The authors describe an integrated multiscale data methodology involving machine-leaning tools applied to the Late Jurassic Upper Jubaila formation outcrop data.
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In this work, a methodology to detect interference from long-term pressure and flow-rate data is developed using multiresolution analysis in combination with machine-learning algorithms.
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Ultimately, the company formerly known as Facebook wants the AI Research SuperCluster system to help it develop artificial intelligence to power the metaverse.