drilling optimization
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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. -
The data-management company has raised its investment in the technology company IWS by $7.9 million, with an agreement to invest an additional $25 million depending on the performance of IWS.
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The authors discuss a study based on twistoffs experienced with bottomhole assembly components during drilling operations and provide recommendations for reduction or elimination of these incidents.
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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.
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This paper describes a collaboration between the operator and a service company that resulted in a successful deployment of an automation platform to manage risks and optimize drilling operations in exploration wells.
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This paper discusses the strategy, approach, and challenges faced in the adoption and implementation of an onsite and remote automated-drilling performance measurement.
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Capitalizing on technology that originated with NASA delivers savings on directional drilling operations.
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The government is offering up to $20 million to test applications that will shorten the time it takes to drill geothermal wells.
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A supervised machine-learning algorithm is developed to classify drilling parameters that increase rate of penetration and bit endurance for use in unconventional fields in Australia.
