forecasting
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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. -
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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SponsoredMOSAIC's advanced Automated Reconciliation to Reserves Workflows enhances accuracy, speeds up processes, and meets the need for precise asset valuation. Equip your reserves teams with reliable information and insights to reduce uncertainty, boost efficiency, and make smarter business decisions.
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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 proposes a data-driven proxy model to effectively forecast the production of horizontal wells with complex fracture networks in shales.
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