Drilling automation
This article is the fourth in a Q&A series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this piece, David Reid, the CTO and CMO for NOV, discusses the evolution and current state of automated drilling systems.
The agreement focuses on improving operational efficiency and consistency through advanced digital tools and real-time data integration.
An innovative approach uses a random-forest-based framework to link logging-while-drilling and multifrequencey seismic data to enable dynamic updates to lithology parameter predictions, enhancing efficiency and robustness of geosteering applications.
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This paper presents the study, resulting recommendations, and a proposed change in standard bottomhole-assembly configurations to reduce service-quality-compromising incidents and productive time lost from jar twistoffs. -
Few oil and gas companies give data science projects the better part of a decade to prove out, but that’s just what this one did.
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This paper presents an approach to optimize the location of wellhead towers using an algorithm based on multiple parameters related to well cost.
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This paper describes the application of learnings from an offshore project in the Caspian to an underground gas storage project to enhance drilling performance.
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The winners of this year’s Drillbotics competition are teams from the University of Stavanger and Clausthal University of Technology. Thirteen teams registered last fall, coming from seven countries spanning four continents.
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CNPC’s record-breaking 11,100-m exploration borehole in the Taklamakan Desert promises to unlock the science of producing oil and gas trapped in the world’s deepest reservoirs.
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The duo’s new services will be initially deployed in Iraq.
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Nabors is connecting Corva’s platform to its universal rig controls and automation platform, allowing apps built and developed in Corva to monitor and control any rig equipped with the platform.
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The authors of this paper present an autonomous directional-drilling framework built on intelligent planning and execution capabilities and supported by surface and downhole automation technologies.
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The authors of this paper discuss a global rate-of-penetration machine-learning model with the potential to eliminate learning curves and reduce time and costs associated with developing a new model for every field.
