Drilling automation
The RoboWell technology for well control will be available globally through Halliburton’s Landmark iEnergy hybrid cloud.
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.
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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The winners of this year’s Drillbotics competition are teams from the University of Stavanger and Clausthal University of Technology. Thirteen teams registered last fall, coming from seven countries spanning four continents. -
CNPC’s record-breaking 11,100-m exploration borehole in the Taklamakan Desert promises to unlock the science of producing oil and gas trapped in the world’s deepest reservoirs.
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The duo’s new services will be initially deployed in Iraq.
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Nabors is connecting Corva’s platform to its universal rig controls and automation platform, allowing apps built and developed in Corva to monitor and control any rig equipped with the platform.
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The authors of this paper present an autonomous directional-drilling framework built on intelligent planning and execution capabilities and supported by surface and downhole automation technologies.
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The authors of this paper discuss a global rate-of-penetration machine-learning model with the potential to eliminate learning curves and reduce time and costs associated with developing a new model for every field.
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The authors of this paper describe a project that demonstrated the feasibility of using deep-learning and machine-learning approaches to introduce camera-based solids monitoring to the drilling industry.
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A joint webinar conducted by the Human Factors and Ergonomics Society and the Society of Petroleum Engineers addressed the role of human factors in automation in the oil and gas industry.
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This paper highlights the potential of machine learning to be used as a tool in assisting the drilling engineer in bit selection through data insights previously overlooked.
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The authors write that simple changes in drillstring design can lead to huge savings in a climate that demands continual reductions in well-delivery time and well costs.
