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This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness. -
This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.
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This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.
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This paper describes a risk-based self-verification process conducted through a bespoke software application.
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The upstream industry has viewed real-time completions as a long-term goal, but the technology is already in use.
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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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The use of real-time wireless downhole pressure gauges proved a valuable alternative to workover operations in two onshore fields in Iraq.
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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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With the right infrastructure and interoperability, subsea resident robotics could unlock more frequent, cost-effective inspections—and a new standard for offshore efficiency.
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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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Bad vibes are being addressed by contractors as operators push to go faster, deeper, and longer with unconventional wells.
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