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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Leading drilling consultant John de Wardt separates hype from reality and explores what’s ahead in this interview with JPT. -
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.
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Technology uptake aimed at optimizing resources, delivering consistency, and augmenting what humans can do.
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This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance.
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We’re thrilled to announce the launch of the 2025 SPE/JPT Drilling and Hydraulic Fracturing Technology Review. This exclusive, official publication will be distributed at three major SPE industry events.
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This paper delves into the evolving landscape of drilling automation, emphasizing the imperative for these systems to go beyond novelty and deliver quantifiable financial value.
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The company has agreed using its innovations in automation in an effort to derisk ultradeep offshore drilling for the Brazilian national oil company.
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The RoboWell technology for well control will be available globally through Halliburton’s Landmark iEnergy hybrid cloud.
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A universal, automated approach to condition-based maintenance of drilling rig mud pumps is developed using acoustic emission sensors and deep learning models for early detection of pump failures to help mitigate and reduce costs and nonproductive time generally associated with catastrophic pump failures.
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