Data & Analytics
Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.
Terra Drone will support Aramco's operations and Saudi Arabia's technological and economic progress.
Organic data governance emphasizes flexibility, stakeholder engagement, and a culture that values data integrity.
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The RoboWell technology for well control will be available globally through Halliburton’s Landmark iEnergy hybrid cloud. -
A self-updating and customizable data-driven strategy for real-time monitoring and management of screenout, integrated with proppant filling index and safest fracturing pump rate, is proposed to improve operational safety and efficiency at field scale.
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Automated workflow unifies geological, completion, and production data to inform speedier, better investment decisions for nonoperated assets.
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Digital transformation presents a crucial opportunity to cut costs across business domains. This review explores unique digital transformation opportunities in the petroleum industry, highlighting valuable business process automations that can drive significant benefits.
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The oil and gas industry can leverage advanced AI and generative AI to bridge knowledge gaps, enhance decision making, and improve safety. These tools will boost efficiency and productivity, leading to a smarter and more resilient industry.
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Both new and old vessels are benefiting from automation processes that can improve operational efficiency, predict downtime, and debottleneck workflows using a flurry of crucial data points.
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The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.
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A seismic prediction model is developed and presented in a case study to simulate the magnitude and timing of triggered seismic events with the intent to manage and mitigate environmental impacts resulting from induced seismicity during subsurface development activities.
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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.
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This paper presents efforts to reduce greenhouse-gas emissions and increase energy efficiency through the use of a real-time monitoring tool on exploration and production operated assets.
