DSDE: In Theory
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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 develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.
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This paper presents an approach using artificial neural networks to predict the discharge pressure of electrical submersible pumps.
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This work provides a new modeling tool, validated against a static-wellbore solver and field data, to estimate and manage downhole temperature in higher-temperature oil, gas, and geothermal wells.
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The authors of this paper propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs.
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The authors of this paper investigate the application of two seismic monitoring methods in monitoring carbon leaks: full waveform inversion and reverse-time migration.
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The main objective of this paper is to investigate the relationship between strain change and pressure change under various fractured reservoir conditions to better estimate conductive fractures and pressure profiles.
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This paper presents an approach for automatic daily-drilling-report classification that incorporates new techniques of artificial intelligence.
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The authors of this paper describe a project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.
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