forecasting
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Best practices are not static; they evolve alongside advancements that redefine what is achievable. -
This article presents a comparative study evaluating four machine-learning approaches, including three deep-learning methods, for forecasting gas and condensate production over a 5-year horizon.
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This paper presents a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight-reservoir forecasting.
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The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.
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Machine learning is refining gas lift production optimization with scalable automated workflow.
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Supervised learning was used to develop an ensemble of models that account for historical production data, geolocation parameters, and completion parameters to forecast production behavior of oil and gas wells.
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The authors of this paper propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment.
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This paper describes a full-field and near-wellbore poromechanics coupling scheme used to model productivity-index degradation against time.
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The agency’s short-term outlook forecasts modest declines in production for the rest of this year.
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