Drilling automation
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.
This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.
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 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.
