Reservoir
The events will be co-located 3–5 May 2027 at Reliant Park in Houston, Texas.
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
This paper presents a study that confirms glass-reinforced-epoxy-lined tubing as a reliable, cost-effective solution for long-term water-injection service in moderate-salinity offshore environments.
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This paper presents a newly developed model to predict the breakdown pressures in cased and perforated wells. -
One of my best moments is to have made the technical case for polymer flooding and to see the fruits of this several decades later. As is so often the case in our industry, my contribution stands alongside those of many others in the intervening years who have all made the EOR journey possible.
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As the country pushes for higher output from its emerging unconventional sector, nature is pushing back. To get better results, operators there are increasing their reliance on technology.
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This paper presents a factory-model approach to improving CT drillout performance that has been used successfully for more than 3 years and has become standard practice.
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This paper discusses the advantages of the in-line quench-and-temper (Q&T) process, which enhances overall CT life and reliability by producing tubing with more-uniform microstructure throughout its entire length, increased material strength, and improved bend-fatigue performance.
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Wireline formation tests are a critical piece of the exploration and appraisal process, yet they come with a degree of uncertainty. The supermajor has tapped a new software developer to see if it can clear things up.
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Indigo Natural Resources, Aethon Energy, and Rockcliff Energy are among the most active operators in the revived Haynesville Shale of North Louisiana and East Texas. And most people outside of the region likely have never heard of them.
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There is every reason to believe that enhanced oil recovery through huff-and-puff injections in US tight-oil plays could be a technical success across large numbers of wells. However, widespread economic success remains uncertain.
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This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale.
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After a long cooling off period, this dry-gas shale play is once again red hot.
