Advanced Machine Learning Applications in Reservoir Management and Forecasting
Tuesday, 21 October
Room 342AD
Technical Session
This section describes Machine Leaning and Deep Learning Applications in oil and gas with focus on decision tree- and neural networks- based algorithms including transformers for solving highly nonlinear and complex problems. The applications include reservoir modeling, reservoir characterization, real-time monitoring and reservoir surveillance, as well as geological gas storage and co—optimization of gas sequestration and oil recovery. These discussed applications include conventional and unconventional formations towards improving efficiency, reducing uncertainty, and increasing safety margin related to the applied processes.
Session Chairpersons
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1400-1425 227946Enhancing Forecasting Accuracy Through Reservoir Modeling With Computational Stratigraphy (compstrat) And Hierarchical Generative Adversarial Networks (gans)
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1425-1450 227905A Noval Workflow Of Near Real-time Fluid Properties Prediction By Integrating Field Data, Eos Model, And Supervised Machine Learning
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1450-1515 228042Horizontal Well Performance Predictions For Carbon Storage Using Deep Neural Networks
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1545-1610 228052Water-alternating-gas And Co2 Storage Optimization Using Time-lapse Geophysical Monitoring And Deep Reinforcement Learning
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1610-1635 228211A Data-driven Deep Learning Framework For Pressure Transient Analysis In Large-scale Underground Gas Storage Systems.
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1635-1700 227993An Efficient Workflow To Quantify The Value Of Information And Uncertainty Reduction From Time-lapse Seismic Data For Monitoring Optimization And Reservoir Characterization In CCS Projects
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Alternate 228029History Matching Geological Models Using An Embed-to-control Observe Deep-learning Reservoir Surrogate
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Alternate 228059Calibrating Stimulated Reservoir Volume (srv) Estimation Using Continuous Wavelet Transform (cwt) And Advanced Deep Learning With Rate Transient Analysis (rta): A Case Study From The Marcellus Shale
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Alternate 227975Intelligent Multi-model Optimization For Fracture Parameter Inversion In Multistage Fractured Horizontal Wells
