Data & Analytics
With the latest addition, the Italian major’s computational capacity passes the exaflop threshold, making the firm the world’s leading company by computing power in the new TOP500 global ranking.
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
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The Landmark DecisionSpace Geosciences software aims to incorporate geoscientists into the company’s digital work flow. -
Artificial intelligence (AI) tools have been used in geological survey methods for many years. Gaining insight into the scale and trends of this implementation could assist surveyors in making informed decisions about buying or developing new technologies.
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The center will allow developers and researchers to test digital and robotic products and services for offshore renewable energy.
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Oil and gas operators such as Shell and Oxy are now employing AI together with a vast network of sensors and other machine-learning software to stamp out problems before they happen.
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Premier Corex and Teverra will be combining their efforts to aggregate data for companies involved in large-scale geothermal projects.
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The artificial intelligence technology is expected to increase understanding of subsurface structures.
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The collaboration expects to redefine methane detection and contribute to emission-reduction efforts across dozens of industries, including energy, agriculture, manufacturing, and transportation.
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This article presents a deep-learning approach, the long short-term memory network, for adaptive hydrocarbon production forecasting that takes historical operational and production information as input sequences to predict oil production as a function of operational plans.
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The authors of this paper describe a continuous monitoring system based on the Internet of Things (IoT) to use methane-concentration sensors permanently installed at facilities and connected to a cloud-based interpretation platform.
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The Robotic Supervision System project is expected to bring autonomous cooperation a step closer for oil and gas operators.
