forecasting
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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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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 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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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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SponsoredIn a partnership with the American Institute of Formation Evaluation, TGS is now the only US data vendor offering report-sourced drillstem tests, available through TGS Well Data Analytics.
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This article presents a deep-learning approach, the long short-term memory network, for adaptive hydrocarbon production forecasting that takes historical operational and production information as input sequences to predict oil production as a function of operational plans.
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Our shifting energy landscape requires a new way to measure the amount of energy that can be extracted from any given source against the energy required to produce and distribute it.
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