AI/machine learning
Schneider Electric says the deal advances its vision of creating intelligent industrial ecosystems that connect physical assets with digital insights across the asset life cycle.
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.
-
Operators tell an audience at the Unconventional Resources Technology Conference how a hybrid expandable liner system and machine-learning-based analysis improve the bottom line. -
Machine learning is refining gas lift production optimization with scalable automated workflow.
-
The Permian’s produced-water challenge presents an opportunity for innovation to pave the way toward a more sustainable future for the industry.
-
The chief operating officer of Chesapeake Energy tells the Unconventional Resources Technology Conference that small wins can pave the path to big achievements.
-
The Norwegian major agrees to use Seeq’s software in an effort to maximize production and enhance efficiency across its assets.
-
This article explores the implementation of artificial intelligence vision for leak monitoring automation in the oil and gas industry and its role in improving safety standards, operational efficiency, and environmental performance.
-
This paper investigates the use of machine learning to rapidly predict the solutions of a high-fidelity, complex physics model using a simpler physics model.
-
This study proposes a hybrid model that combines the capacitance/resistance model, a machine-learning model, and an oil model to assess and optimize water-alternating-gas (WAG) injectors in a carbonate field.
-
The partnership aims to use artificial intelligence and advanced robotics to accelerate the adoption of technologies for predictive maintenance.
-
The new AIQ ownership structure will see Presight acquire 51% shareholding, with ADNOC retaining 49% and receiving a 4% stake in Presight. AIQ will continue to operate as a standalone company.
