Data & Analytics
Shell has demonstrated drone-in-a-box operations from its Mars floating production unit, becoming the first operator in the region to secure FAA approval for self-approved offshore beyond-visual-line-of-sight flights. JPT Senior Technology Editor Jennifer Pallanich visited Shell Technology Center Houston to see the system in action.
Panelists at the SPE Subsea Well Intervention Symposium discussed where AI is delivering value today, where risks remain, and how engineers can best determine its usefulness.
As digital technologies become commonplace, industry leaders say core engineering knowledge remains essential for making informed decisions and avoiding costly mistakes.
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Imagine the time when we are no longer concerned about the digital-transformation issues we face today, such as data availability, security, and many others. This would be the time when companies make the best out of digital infrastructure. This era might be here sooner than expected. -
Incorporating imagination into AI agents has long been an elusive goal of researchers in the space. Imagine AI programs that are able not only to learn new tasks but also to plan and reason about the future.
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The rise of automation has been a common theme in stories that touch almost every business sector. One of the places where automation has shown the most value has been in enterprise security, where it can reduce costs and mitigate vulnerabilities in many instances.
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In addition to the well-recognized elements of digital transformation such as real-time monitoring, remote intelligence, and extraction of insights from data, there is a need to evolve the industry hardware through application of enhanced edge computing.
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Field examples presented in the complete paper describe principles of data acquisition with a sand-detection tool when run in combination with a production logging string and results of logging in slightly deviated wells completed with sand screens.
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Automated image-processing algorithms can improve the quality and speed in classifying the morphology of heterogeneous carbonate rock. Several commercial products have produced petrophysical properties from 2D images and, to a lesser extent, from 3D images.
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The complete paper provides an approach using machine-learning and sequence-mining algorithms for predicting and classifying the next operation based on textual descriptions.
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The complete paper describes an automated machine-learning approach to determine the spatial variation in decline type curves for shale gas production, based on existing data of production, completion, and geological parameters.
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Imagine the time when we are no longer concerned about the digital-transformation issues we face today, such as data availability, security, and many others. This would be the time when companies make the best out of digital infrastructure. This era might be here sooner than expected.
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A recent panel discussion highlighted the industry’s progress in achieving significant digital advances. Barriers remain, however, and the measurement of success is being defined in this evolving technological step change.
