Drilling automation
Sponsored
With operators under pressure to deliver more energy with fewer resources, predictable drilling performance is more important than ever. Discover how a unified digital workflow can turn drilling data into better decisions and consistent execution—providing the foundation for autonomous drilling and more predictable, profitable, and productive wells at scale.
Current classifications often do not capture the complexity of autonomy in drilling systems. By integrating concepts from aerospace, control theory, and other high-risk industries, this study presents a quantitative framework for systematically assessing and comparing levels of drilling autonomy.
Winners of Alberta's Drilling Technology Challenge cover a range of technologies from robotics to enhance drilling rig safety to AI-enabled energy management, downhole sensing, well navigation, hybrid power systems, and geothermal energy.
-
The company said that its AD-300, a 50-m-high automated island drilling rig that walks between wells, was delivered 3 months ahead of schedule. -
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.
-
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.
-
Autonomous drilling through managed pressure drilling (MPD) at the Atlantis field has given the operator confidence to scale the method.
-
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.
-
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.
-
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.
Page 1 of 17