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Proceedings Papers

Use of Clustering Techniques for Automated Lumping of Components in Compositional Models

Michel A. Cancelliere, J. Andino Saint Antonin
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212211-MS
https://doi.org/10.2118/212211-MS
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Proceedings Papers

A Data-Driven Deep Learning Framework for Microbial Reaction Prediction for Hydrogen Underground Storage

Klemens Katterbauer, Abdallah Al Shehri, Abdulaziz Qasim, Ali Yousif
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212187-MS
https://doi.org/10.2118/212187-MS
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View Paper titled, A Data-Driven Deep Learning Framework for Microbial Reaction Prediction for Hydrogen Underground Storage
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Proceedings Papers

Efficient Adaptation and Calibration of Ad joint-Based Reduced-Order Coarse-Grid Network Models

S. Krogstad, Ø. A. Klemetsdal, K. -A. Lie
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212207-MS
https://doi.org/10.2118/212207-MS
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View Paper titled, Efficient Adaptation and Calibration of Ad joint-Based Reduced-Order Coarse-Grid Network Models
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Proceedings Papers

A Fast History Matching and Optimization Tool and its Application to a Full Field with More than 1,000 Wells

Guotong Ren, Zhenzhen Wang, Yuanbo Lin, Tsubasa Onishi, Xiaoyue Guan, Xian-Huan Wen
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212188-MS
https://doi.org/10.2118/212188-MS
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View Paper titled, A Fast History Matching and Optimization Tool and its Application to a Full Field with More than 1,000 Wells
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Proceedings Papers

A Novel Machine-Learning Assisted Phase-Equilibrium Calculation Model for Liquid-Rich Shale Reservoirs

Fangxuan Chen, Sheng Luo, Shihao Wang, Hadi Nasrabadi
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212193-MS
https://doi.org/10.2118/212193-MS
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View Paper titled, A Novel Machine-Learning Assisted Phase-Equilibrium Calculation Model for Liquid-Rich Shale Reservoirs
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Proceedings Papers

Performance Benchmarking of Different Methods to Solve Gauss-Newton Trust Region Subproblems

Guohua Gao, Horacio Florez, Jeroen Vink, Carl Blom, Terence Wells, Fredrik Saaf
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212180-MS
https://doi.org/10.2118/212180-MS
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View Paper titled, Performance Benchmarking of Different Methods to Solve Gauss-Newton Trust Region Subproblems
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Proceedings Papers

Nonlinearly Constrained Life-Cycle Production Optimization Using Sequential Quadratic Programming (SQP) With Stochastic Simplex Approximated Gradients (StoSAG)

Quang Nguyen, Mustafa Onur, Faruk Omer Alpak
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212178-MS
https://doi.org/10.2118/212178-MS
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View Paper titled, Nonlinearly Constrained Life-Cycle Production Optimization Using Sequential Quadratic Programming (SQP) With Stochastic Simplex Approximated Gradients (StoSAG)
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Proceedings Papers

A GPU-Accelerated Simulator for Challenging Extreme-Scale Geomechanical Models

Matteo Frigo, Giovanni Isotton, Carlo Janna, Nicolò Spiezia, Massimiliano Ferronato, Andrea Franceschini, Annachiara Filippini, Giovanni Scrofani
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212198-MS
https://doi.org/10.2118/212198-MS
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View Paper titled, A GPU-Accelerated Simulator for Challenging Extreme-Scale Geomechanical Models
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Proceedings Papers

Guided Deep Learning Manifold Linearization of Porous Media Flow Equations

Marcelo J. Dall'Aqua, Emilio J. R. Coutinho, Eduardo Gildin, Zhenyu Guo, Hardik Zalavadia, Sathish Sankaran
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212204-MS
https://doi.org/10.2118/212204-MS
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View Paper titled, Guided Deep Learning Manifold Linearization of Porous Media Flow Equations
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Proceedings Papers

A Physics-Informed Neural Network for Temporospatial Prediction of Hydraulic-Geomechanical Processes

Chi Zhang, Shihao Wang, Yu-Shu Wu
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212202-MS
https://doi.org/10.2118/212202-MS
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View Paper titled, A Physics-Informed Neural Network for Temporospatial Prediction of Hydraulic-Geomechanical Processes
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Proceedings Papers

An Adaptive Newton–ASPEN Solver for Complex Reservoir Models

K. -A. Lie, O. Møyner, Ø. A. Klemetsdal
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212199-MS
https://doi.org/10.2118/212199-MS
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View Paper titled, An Adaptive Newton–ASPEN Solver for Complex Reservoir Models
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Proceedings Papers

Feature Extraction in Time-Lapse Seismic Using Deep Learning for Data Assimilation

Rodrigo Exterkoetter, Gustavo Rachid Dutra, Leandro Passos de Figueiredo, Fernando Bordignon, Alexandre Anozé Emerick, Gilson Moura Silva Neto
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212196-MS
https://doi.org/10.2118/212196-MS
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View Paper titled, Feature Extraction in Time-Lapse Seismic Using Deep Learning for Data Assimilation
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Proceedings Papers

Reservoir Connectivity Identification and Robust Production Forecasting Using Physics Informed Machine Learning

Masahiro Nagao, Akhil Datta-Gupta, Tsubasa Onishi, Sathish Sankaran
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212201-MS
https://doi.org/10.2118/212201-MS
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View Paper titled, Reservoir Connectivity Identification and Robust Production Forecasting Using Physics Informed Machine Learning
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Proceedings Papers

Deep Learning-Based Multiresolution Parameterization for Spatially Adaptive Model Updating

Mahammad Valiyev, Syamil Mohd Razak, Behnam Jafarpour
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212167-MS
https://doi.org/10.2118/212167-MS
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View Paper titled, Deep Learning-Based Multiresolution Parameterization for Spatially Adaptive Model Updating
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Proceedings Papers

Application of Deep Neural Networks to the Operator Space of Nonlinear PDE for Physics-Based Proxy Modelling

George Hadjisotiriou, Kiarash Mansour Pour, Denis Voskov
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212217-MS
https://doi.org/10.2118/212217-MS
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View Paper titled, Application of Deep Neural Networks to the Operator Space of Nonlinear PDE for Physics-Based Proxy Modelling
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Proceedings Papers

Integrated Framework for Optimization of Horizontal/Deviated Well Placement and Control for Geological CO 2 Storage

Amy Zou, Louis J. Durlofsky
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212228-MS
https://doi.org/10.2118/212228-MS
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View Paper titled, Integrated Framework for Optimization of Horizontal/Deviated Well Placement and Control for Geological CO 2 Storage
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Proceedings Papers

A Quasi-Newton Method for Well Location Optimization Under Uncertainty

Esmail Eltahan, Faruk Omer Alpak, Kamy Sepehrnoori
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212212-MS
https://doi.org/10.2118/212212-MS
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View Paper titled, A Quasi-Newton Method for Well Location Optimization Under Uncertainty
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Proceedings Papers

Deep Learning-Based Disentangled Parametrization for Model Calibration Under Multiple Geologic Scenarios

Junjie Yu, Behnam Jafarpour
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212177-MS
https://doi.org/10.2118/212177-MS
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View Paper titled, Deep Learning-Based Disentangled Parametrization for Model Calibration Under Multiple Geologic Scenarios
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Proceedings Papers

Adding GPU Acceleration to an Industrial CPU-Based Simulator, Development Strategy and Results

Hui Cao, Rustem Zaydullin, Terrence Liao, Neil Gohaud, Eguono Obi, Gilles Darche
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203936-MS
https://doi.org/10.2118/203936-MS
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View Paper titled, Adding GPU Acceleration to an Industrial CPU-Based Simulator, Development Strategy and Results
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Proceedings Papers

Physics Inspired Machine Learning for Solving Fluid Flow in Porous Media: A Novel Computational Algorithm for Reservoir Simulation

Chico Sambo, Yin Feng
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203917-MS
https://doi.org/10.2118/203917-MS
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View Paper titled, Physics Inspired Machine Learning for Solving Fluid Flow in Porous Media: A Novel Computational Algorithm for Reservoir Simulation
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