Unconventional/complex reservoirs
A stimulation recipe that has unlocked tight-rock resources all over the world appears to work in Saudi Arabia too.
As production dynamics evolve over the full life cycle of a tight-oil play, a single artificial lift method may not be the most cost-effective solution.
This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
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The authors of this paper present an artificial-lift timing and selection work flow using a hybrid data-driven and physics-based approach that incorporates routinely available pressure/volume/temperature, rate, and pressure information. -
This paper focuses on characterization of fracture hits in the Eagle Ford, methods to predict their effects on production, and mitigation techniques.
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SponsoredWhat are we, in the upstream oilfield service companies, doing about it? The Middle East upstream oil and gas industry is shifting its focus to unconventional and tight-gas resource development.
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Diamondback Energy has agreed to spin off its water operations. Now, who’s next?
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On a pro forma basis, the mineral and royalty arm of the Midland-based oil company owns interests covering more than 32,000 net acres in the Permian Basin.
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SponsoredThe data that comes with mapping flow behavior at the stage level of unconventional wells was once accessible only through the installation of costly and intrusive diagnostic methodologies like fiber optic or running production logging. New-generation FloTrac ultrahigh-resolution nanoparticle tracer technology with subatomic spectroscopic measurement techniques now de…
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The authors of this paper analyze a robust, well-distributed parent/child well data set using a combination of available empirical data and numerical simulation outputs to develop a predictive machine-learning model.
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This paper summarizes a collaborative industry study to compare observations between shale-play data sets and basins, develop general insights into parent/child interactions, and provide customized economic optimization recommendations.
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In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.
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New studies are a reminder that the effort to identify, classify, and nullify frac hits remains paramount to the future of the unconventionals business.
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