Drilling automation
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.
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.
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This paper describes how new technology was implemented and deployed through a downhole acoustic network through a sequence of runs in complex North Sea wells under managed-pressure conditions. -
This paper discusses how oil and gas companies are using a new generation of AI-driven applications powered by computational-knowledge graphs and AI algorithms to create a digital knowledge layer for oil and gas wells that provides a timeline of significant well events.
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Drilling systems automation (DSA) is moving into commercial activities on a broad front. Equipment suppliers are delivering automated drilling-control systems, and everyone (equipment suppliers, service companies, drilling contractors, operators) is delivering systems-automation applications.
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The complete paper presents a process used to successfully implement a rig-based drilling advisory system (RDAS) across a mixed group of rig contractors.
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The complete paper presents a drilling semantic framework that allows software solutions to achieve automatic and versatile self-configuration.
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In the current work, a rig advisory system is developed to continually improve rate of penetration (ROP) and drilling performance. An intelligent drilling advisory system (IDAS), based on a soft closed-loop solution with multiple regression analysis has been established.
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Hess is testing whether it can drive drilling improvement by combining drilling rigs equipped with automated functions and humans determined to find a way to beat the programmed drilling.
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Rigs drilling faster earn less money per foot because they are contracted by the day. But at least they are still working. Now service companies are developing new rigs with more automated functions, and want increased rates based on the productivity gains achieved.
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Corva’s challenge is to change the behavior of drillers who work for somebody else. The fast-growing company has no shortage of users.
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Drilling change requires training drillers. Leadership matters, as does motivation, engaging displays, and understanding office politics. Four different looks at the human side of drilling productivity improvement.
