Drilling
This paper presents the application of degradable particulate-diverter technology to improve oil production by effectively diverting stimulation treatment fluids away from high-water-cut, high-permeability, high-temperature producing intervals or zones.
This paper presents the methodology for developing drill-in organophilic clay-free invert-emulsion-fluid technology that enhances well production through fundamental geological studies and fluid testing.
This paper presents the large-scale deployment and operational integration of an advanced, automated rheometer in the Ghawar gas field, one of the world’s most prolific and technically complex hydrocarbon developments.
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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. -
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.
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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.
