Drilling automation
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.
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This paper describes the application of learnings from an offshore project in the Caspian to an underground gas storage project to enhance drilling performance. -
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.
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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.
