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 discusses the results of driller stress tests and the implementation of a system that assists the operator in kick detection, space out, and preparation for well shut-in. -
The authors describe a drilling-systems automation roadmap for a transition from humans to automation in the general drilling space.
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The authors describe a platform that integrates advanced data analytics and hydraulic modeling in real time for managed-pressure-drilling applications.
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Totally automated drilling today looks like a robot doing all the heavy lifting on a drilling floor. By 2025, there may no longer be anything surprising about it.
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A supervised machine-learning algorithm is developed to classify drilling parameters that increase rate of penetration and bit endurance for use in unconventional fields in Australia.
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The failure of the Raptor rig to drill its first-ever well offers a short history of the challenges that came with creating the first automated drilling rig.
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Drilling automation is not “there” yet, but it no longer seems like a pipe dream.
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This paper describes how severe rig limitations were overcome through an optimization plan in which an optimal bottomhole assembly was designed and drilling practices were customized.
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This paper presents a methodology that aims to allows the anticipation of problems such as mechanically stuck pipe or lockup situations when running casing or completion strings in hole.
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The 2021–2022 Drillbotics competition will require the contestants to integrate human factors engineering considerations into their automated drilling rigs for the first time.
