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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Improved bit and bottomhole-assembly technologies and designs have helped turn what used to be record-breaking drilling runs into routine expectations. -
The project with ExxonMobil used closed-loop drilling and digital well-construction technologies.
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Autonomous drilling through managed pressure drilling (MPD) at the Atlantis field has given the operator confidence to scale the method.
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This paper presents a multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques to deliver consistently high performance in a scalable manner for sliding.
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This paper proposes a novel approach toward drilling maximum-reservoir-contact wells by integrating automated drilling and geosteering software to control the downhole bottomhole assembly, thereby minimizing the need for human intervention.
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In this study, a method was developed to analyze the effects of drilling through transitions on bit-cutting structures and construct an ideal drilling strategy using a detailed drilling model.
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This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.
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This research aims to develop a fluid-advisory system that provides recommendations for optimal amounts of chemical additives needed to maintain desired fluid properties in various drilling-fluid systems.
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The SPE Drilling Systems Automation Technical Section has a new name—and a bigger mission. Discover how DSATS is evolving beyond automation to drive smarter, more connected, and more human-centered drilling systems for the future.
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This paper presents the development of a digital tool for automatically analyzing the readiness of a borehole to accept casing. The tool integrates data-driven and physics-based models to indicate locations of risk along the wellbore.
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