AI/machine learning
The companies said they plan to start deploying digital twin technologies in Oman this year.
Oil and gas experts encourage human/AI partnerships that can “supercharge” capabilities to create competitive advantages.
This paper reviews the motivation and development of response-based forecasting from the perspective of the authors, reviewing examples and processes that have served as validation and led to modeling refinement.
-
The service giant shares new details about its automated fracturing spreads that slash human operator workload by 88%. -
The trial phase of the agentic program used AI agents and combined large-language-model technology with data collected from more than 15% of ADNOC’s onshore and offshore wells.
-
SLB said it plans to integrate INT’s technology into its digital data and artificial intelligence platforms.
-
Chevron’s announcement comes on the heels of ExxonMobil’s announcement in December of a similar project to deliver natural gas-fueled electricity to US data centers.
-
These papers provided insights and advances into field-operations automation, machine-learning-assisted petrophysical characterization, and fluid-distribution analysis in unconventional assets.
-
In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.
-
The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.
-
From optimizing drilling performance to enhancing worker safety, computer vision can change how the industry works.
-
A recent survey conducted by Rackspace Technology reveals new attitudes about using the cloud, including a change from using the public cloud to using private, on-site clouds or a hybrid of the two.
-
This study examines the implementation of a predictive maintenance method using artificial intelligence and machine learning for offshore rotating production-critical equipment. Conducted over 2 years at Murphy Oil’s deepwater platforms in the Gulf of Mexico, the project aimed to detect equipment issues early, reduce downtime, and streamline maintenance processes.
