AI/machine learning
Aurora Innovation and Detmar Logistics have inked a deal for 30 autonomous trucks that will begin hauling sand in the region next year.
Sustainability in reservoir management emerges not from standalone initiatives but from integrated, data-driven workflows, where shared models, closed-loop processes, and AI-enabled insights reduce fragmentation and make sustainable performance a natural outcome.
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In oil and gas operations, every decision counts. For more than 2 decades, SiteCom has been the trusted digital backbone for well operations worldwide, driving insight, collaboration, and efficiency.
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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.
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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.
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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.
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The partnership aims to use artificial intelligence and advanced robotics to accelerate the adoption of technologies for predictive maintenance.
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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.
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This paper presents the design and development of a prototype intelligent water-injection and smart allocation tool aimed at achieving autonomous waterflood operations.
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Developing alternative power supplies with wide-scale reliability, dependability, and minimization or elimination of GHG emissions within feasible capex/opex scenarios is the brass ring of sustainability and energy security—and data are helping us get there.
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In the pursuit of sustainable industrial operations, three pivotal objectives emerge: risk reduction, safety assurance, and cost minimization. Integrating these objectives into digital transformation strategies enables operators to effectively manage emissions and achieve success.
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Supervised learning was used to develop an ensemble of models that account for historical production data, geolocation parameters, and completion parameters to forecast production behavior of oil and gas wells.
