AI/machine learning
The companies said they plan to start deploying digital twin technologies in Oman this year.
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.
This paper presents a novel workflow with multiobjective optimization techniques to assess the integration of pressure-management methodologies for permanent geological carbon dioxide storage in saline aquifers.
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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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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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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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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.
