AI/machine learning
This paper discusses a comprehensive hybrid approach that combines machine learning with a physics-based risk-prediction model to detect and prevent the formation of hydrates in flowlines and separators.
This paper explains that the discovery of specific pressure trends, combined with an unconventional approach for analyzing gas compositional data, enables the detection and prediction of paraffin deposition at pad level and in the gathering system.
Adaptability, collaboration, and digital technologies are all pages in Aramco’s oilfield R&D playbook.
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The supermajor’s Energy Outlook 2025 suggests geopolitical fragmentation could tilt the balance of the energy trilemma toward energy security and away from sustainability. -
The companies said they plan to start deploying digital twin technologies in Oman this year.
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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.
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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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This paper introduces a technology for offshore pipeline inspection centered on an autonomous robotic system equipped with underwater computer vision and edge-computing capabilities.
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Oil and gas experts encourage human/AI partnerships that can “supercharge” capabilities to create competitive advantages.
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Deploying artificial intelligence across an enterprise requires thinking beyond the pilot.
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The fifth edition of the SPE Europe Energy GeoHackathon, beginning on 1 October, focuses on how data science can advance geothermal energy and drive the energy transition.
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This paper presents the development of a robust, physics-based, and data-driven workflow for modeling mud loss in fractured formations and predicting terminal mud loss volume and time, as well as equivalent hydraulic fracture aperture.
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This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.
