Drilling automation
This study explores the use of autoencoder models with convolutional neural networks to present a framework and prototype for early and accurate kick detection during offshore oilwell drilling.
This paper presents the first global application of autonomous drilling in deepwater and the journey to reach optimal drilling parameters, integrating proprietary tools from the project’s business partners.
Drilling experts recently shared candid views on what will be required for their segment of the upstream business to move to the next stage of development.
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This paper describes an experimentation trial deploying and operating a computer-vision system on a deepwater rig to measure drilled cuttings in real time using a remotely monitored camera system. -
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.
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The agreement focuses on improving operational efficiency and consistency through advanced digital tools and real-time data integration.
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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 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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New case studies highlight how artificial intelligence, advanced hardware, and innovative business models are enabling success in drilling automation.
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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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Leading drilling consultant John de Wardt separates hype from reality and explores what’s ahead in this interview with JPT.
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In this paper, bottomhole-assembly lateral behavior is analyzed using different types of computations, including static, dynamic, frequency-based, and time-based.
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Digitalization and automation of the drilling process drive the need for an interoperability platform in a drilling operation, where a shared definition and method of calculation of the drilling process state is a fundamental element of an infrastructure to enable interoperability at the rigsite.
