Drilling
The collaboration aims to provide a dedicated land rig for geothermal wells in the US.
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.
Operators aren’t rushing to drill, even as the closure of the Strait of Hormuz drives oil prices up.
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This paper describes an openhole wireline-logging operation in a deepwater Gulf of Mexico well in a high-pressure/high-temperature slimhole environment using water-based reservoir drilling fluid. -
The authors of this paper present results of a study that examined formation-damage mechanisms caused by drilling fluids in tight reservoirs in onshore oil fields in Abu Dhabi.
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The paper presents predicted vs. measured wear for six wells in the Culzean field, a high-pressure/high-temperature gas condensate field in the central North Sea.
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The authors of this paper present a laboratory-based model to determine the detachment of authigenic and detrital particles in formation damage.
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The authors of this paper discuss a biosurfactant treatment that offers an economical method for remediation of formation damage caused by high-molecular-weight paraffin wax deposition in porous media.
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This paper reviews the successful application of a mud-cooling and managed-pressure-drilling system in a high-pressure/high-temperature well to explore the potential of a Mesozoic carbonate platform in the Nile Delta of Egypt.
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Environmental report recommends shrinking the Alaska project to three drilling sites from the five initially proposed by ConocoPhillips.
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With the arrival of the digital age, solutions in big data, automation, and artificial intelligence are rapidly opening the door to a deeper and more-comprehensive understanding of drilling operations around the world.
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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.
