Data & Analytics
As managed pressure drilling gains traction, its principles are influencing a wider range of well-construction activities.
From AI-enabled walking rigs to subsea drones and autonomous inspection robots, robotics is rapidly moving from pilot projects to field deployment. The technology promises not only greater efficiency but also a fundamental shift in how the industry approaches safety and asset management.
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
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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. -
The deal follows Blackstone’s recent moves in gas-fired power and a pipeline venture with top US gas producer.
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The collaboration will see TGS’ software platform implemented throughout the carbon value chain at the Northern Lights project.
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By closely monitoring its subsea boosting system, Shell extended maintenance intervals and safely postponed pump replacement at its ultradeepwater Stones field.
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This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.
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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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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 work introduces a fast, methodical approach to detect liquid loading using easily available field data while avoiding traditional assumptions and to determine critical gas rates directly from field data.
