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This paper discusses and demonstrates the limitations of quantitative risk assessment (QRA) with respect to the usefulness of the concept in managing day-to-day and emerging risks as well as the effect of change.
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Emerging solutions could solve current subsea pain points, while a new taxonomy system could clarify the capabilities of the expanding domain of underwater vehicles.
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This study explores enhancing gas production through a novel combination of prestimulation using a coiled tubing unit and high-rate matrix acidizing.
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This paper describes a tool that complements predictive analytics by evaluating top health, safety, and environment risks and recommends risk-management-based assurance intervention.
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Bad vibes are being addressed by contractors as operators push to go faster, deeper, and longer with unconventional wells.
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Big oil companies are backing direct lithium extraction while the market flashes warning signs.
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As LNG projects sanctioned earlier this decade come onstream, a shortage of new final and pre-final investment decisions threatens to leave the project pipelines dry at a time when global LNG demand is forecast to surge over the next 15 years.
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A New Texas Two-Step: Why One Eagle Ford Producer Is Using Hydrocarbons for Well Stimulation and EORBlackBrush Oil & Gas tells JPT about its use of natural gas liquids and condensate to increase oil recovery in horizontal shale wells.
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The deal marries Baker’s core competencies in rotating equipment, flow control and digital technology with Chart’s heat transfer, air- and gas- handling, and process technologies expertise.
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Rystad Energy reports that developers are increasingly turning to floating LNG to reduce costs and accelerate project timelines in response to growing gas demand.
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The first of four planned Phase 1 wells is flowing from the region’s second 20k-psi deepwater project.
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Traditionally, the drilling industry has relied on high-fidelity thermal simulators to predict downhole temperature for different operational scenarios. Though accurate, these models are too slow for real-time applications. To overcome this limitation, a deep-learning solution is proposed that enables fast, accurate prediction of downhole temperatures under a wide ran…
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