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 highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance. -
Technology uptake aimed at optimizing resources, delivering consistency, and augmenting what humans can do.
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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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The SPE Drilling and Wells Interoperability Standards group proposes a dual-path strategy to overcome the technical and commercial barriers facing the advancement of drilling automation.
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This paper describes an autonomous drilling approach using an autocurve-drilling mode to automatically drill curved sections without human intervention and complete autonomous well construction.
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This paper presents an approach for automatic daily-drilling-report classification that incorporates new techniques of artificial intelligence.
