Drilling automation
This comprehensive review of stuck pipe prediction methods focuses on data frequency, approach to variable selection, types of predictive models, interpretability, and performance assessment with the aim of providing improved guidelines for prediction that can be extended to other drilling abnormalities, such as lost circulation and drilling dysfunctions.
New case studies highlight how artificial intelligence, advanced hardware, and innovative business models are enabling success in drilling automation.
This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.
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The contract is helping to solidify Europe’s offshore sector as the focal point for the rise of automated drilling technology. -
Drilling systems automation requires a digital backbone. One segment of that backbone is the interval between the drill bit and the surface. Wired pipe removes both the bandwidth and latency barriers of the available measurement-while-drilling telemetry systems.
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In this paper, the application of a real-time T&D model is demonstrated. The process of T&D analysis was automated, and the time and cost required to run physical models offline was reduced or, in some cases, eliminated.
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This paper presents a case history of drilling automation system pilot deployment, including the use of wired drillpipe, on an Arctic drilling operation.
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This paper describes the progress of directional-drilling-automation systems along the cognitive functions and levels of automation as defined by the Levels of Automation Taxonomy (LOAT) hierarchy introduced by the Drilling Systems Automation Roadmap Industry Initiative.
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Falling oil prices are the acid test of drilling efficiency. SPE Technical Director Jeff Moss of ExxonMobil talks about ways to build in lasting savings as part of this special report.
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The latest example of the offshore sector's march toward automated wellbore construction will take shape later this year in the North Sea.
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A contest where teams of college students design and build an automated drilling rig able to deal with hazardous obstacles in a test block, showed how a small change can be engineered to matter.
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This paper proposes a metric for quantifying drilling efficiency and drilling optimization that is computed by use of a Bayesian network.
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Signs of maturity in drilling systems automation are evident in the success stories filtering out of technical conferences and in the attraction of top university talent to an annual, international, drilling-systems-automation contest.
