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Keywords: prediction
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Proceedings Papers
Incremental Machine Learning for Near-Well Prediction: A Non-Linear Preconditioning Approach to Faster History Matching
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223843-MS
... introduces an innovative methodology, building on recent work on the "Hybrid Newton" method, that leverages machine learning for non-linear preconditioning to accelerate the history matching process, with a specific focus on incremental learning for near-well prediction. Our approach integrates machine...
Proceedings Papers
Implementing a Duchon Spline Based on Gauss-Newton Distributed Optimizer for Coupled Flow and Geomechanics
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223866-MS
...-logs. We employ a local Gauss-Newton (GN) optimizer that requires about half the number of simulations compared to equivalent gradient-based or serial optimizers. We approximate the sensitivity matrix by interpolating the resulting predictions with Duchon splines, i.e., radial basis functions, opposite...
Proceedings Papers
Grid-Orientation Effects in the 11th SPE Comparative Solution Project Using Unstructured Grids and Consistent Discretizations
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223885-MS
... is essential to predict how much CO2 will be effectively trapped and how quickly this trapping occurs, and may necessitate fine grids in regions with steep gradients, while other areas with minor variations can be more coarsely resolved. Additionally, the expected gravity-driven convective mixing at field...
Proceedings Papers
Deep-Learning-Based History Matching Framework Using an Embed-To-Control Observe Reservoir Surrogate
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223904-MS
... variables from a high-dimensional space to a low-dimensional space, a linear transition network for predicting the evolution of state variables in the latent space, and a linear transition output for extending predictions to well output evolution over time. The E2CO framework is implemented using Proper...
Proceedings Papers
Coupled Graph Neural Network and Fourier Neural Operator Architecture for Ensemble Workflows in 3d Reservoir Simulation
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223907-MS
... to use in history matching, optimization and uncertainty quantification workflows. We propose a novel machine learning architecture to auto-regressively predict state variables and well outputs of a reservoir for an ensemble of cases with varying well configurations. This new approach constructs a Graph...
Proceedings Papers
Application of Embed-to-Control Observe Deep-Learning-Based Reservoir Surrogate in CO 2 Storage Monitoring and Multi-Objective Optimization
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223914-MS
... This paper presents a carbon dioxide (CO 2 ) storage application of a data-driven approach in forecasting reservoir performance (via the prediction of state variables and well outputs) of a compositional fluid system and multi-objective optimization under geological uncertainty using a deep-learning-based...
Proceedings Papers
Application of Ensemble-Based Forecast Calibration with Data-Space Inversion for Production Optimization
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223878-MS
... DSI is an approach that conditions the model's predictions for either history matching (HM) or future projections through statistical models or machine learning, without repeatedly running the reservoir models (models are run only to create the prior. Sun and Durlofsky (2017 ) delineated...
Proceedings Papers
HPHT Reservoir Acid Gas: Experimental Determination of Z-Factor and Equation of State Modeling
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223912-MS
... and empirical correlation predictions of the gas compressibility factor increases at higher pressures and temperatures, leading to uncertainties in reservoir performance and production modeling. This work includes experimental determination of Z-factors across pressure and temperature ranges, selection...
Proceedings Papers
Coupled Flow-Geomechanics Surrogate Model with Flexible Boundary Conditions for Geological CO 2 Storage Using Fourier Neural Operator Based Gated Recurrent Network
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 25–27, 2025
Paper Number: SPE-223871-MS
... Spatial-temporal modeling plays a critical role in understanding and predicting the behavior of complex systems, such as geological CO2 storage in our study. These systems involve interactions across both spatial domains and temporal scales, making accurate and efficient modeling crucial...
Proceedings Papers
A New Phase-Labeling Method Based on Machine Learning for CO2 Applications
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212254-MS
... part of this paper therefore describes a new phase labeling method that uses both the critical temperature and saturation pressure predictions from the ML models to generate accurate labels. Results are presented for CO 2 rich fluids. We show that this ML approach can result in accurate labeling...
Proceedings Papers
Guided Deep Learning Manifold Linearization of Porous Media Flow Equations
Available to PurchaseMarcelo J. Dall'Aqua, Emilio J. R. Coutinho, Eduardo Gildin, Zhenyu Guo, Hardik Zalavadia, Sathish Sankaran
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, March 28–30, 2023
Paper Number: SPE-212204-MS
... Abstract Integrated reservoir studies for performance prediction and decision-making processes are computationally expensive. In this paper, we develop a novel linearization approach to reduce the computational burden of intensive reservoir simulation execution. We achieve this by introducing...
Proceedings Papers
JFTS+H: A Julia-Based Parallel Simulator for the Description of the Coupled flow, Thermal and Geochemical Processes in Hydrate-Bearing Geologic Media
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203953-MS
... component. The mass accumulation term is then given by (6) ∑ β = A , G , I , H ϕ S β ρ β X β κ , κ = w , m , h , i reservoir characterization flow assurance flow in porous media ada cluster prediction fluid dynamics...
Proceedings Papers
Use of Horizontal Drift-Flux Models For Simulating Wellbore Flow in SAGD Operations
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203955-MS
... are presented that illustrate homogeneous and drift-flux flow model differences for various well scenarios. thermal method reservoir simulation well model steam-assisted gravity drainage prediction well trajectory light blue circle enhanced recovery upstream oil & gas triangle homogeneous...
Proceedings Papers
History Matching Complex 3D Systems Using Deep-Learning-Based Surrogate Flow Modeling and CNN-PCA Geological Parameterization
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203924-MS
... in this work. machine learning reservoir simulation artificial intelligence deep learning fluid dynamics neural network upstream oil & gas prediction geomodel es-mda procedure application time step evaluation flow in porous media history matching parameterization surrogate model...
Proceedings Papers
Bayesian Long-Short Term Memory for History Matching in Reservoir Simulations
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203976-MS
... successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and precision of the workflow. This approach provides an efficient and high-quality...
Proceedings Papers
Reduced-Order Modeling for Multiphase Flow Using a Physics-Based Deep Learning
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203965-MS
... ( Rewieński and White 2003 ). Several people used this POD-TPWL in subsurface applications. See ( Cardoso and Durlofsky 2010 ), ( He and Durlofsky 2014 ), and ( Jin and Durlofsky 2018 ). Reservoir simulators are the most widely used tools in reservoir management and subsurface flow predictions. Accurate...
Proceedings Papers
Upscaling of Realistic Discrete Fracture Simulations Using Machine Learning
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203962-MS
... features in the CNN structure which are crucial for achieving high accuracy of predictions for the DPDP model closures, and put forward the corresponding CNN architectures. Obtaining a suitable training dataset is challenging because i ) it requires a dedicated effort to map the fracture geometries; ii...
Proceedings Papers
Physics Inspired Machine Learning for Solving Fluid Flow in Porous Media: A Novel Computational Algorithm for Reservoir Simulation
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203917-MS
... is investigated. The results show that the PIML performs well, giving good results comparable to analytical solution. Further, we examined the potential of PIML approach in handling fluxes (sink and source terms). Our results demonstrate that the PIML fail to provide acceptable prediction for no-flow boundary...
Proceedings Papers
Efficient Localized Nonlinear Solution Strategies for Unconventional-Reservoir Simulation with Complex Fractures
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-203987-MS
... and convergence behavior. flow in porous media reservoir simulation case 2 modeling & simulation complex reservoir average ratio timestep fracture network locality equation of state fluid dynamics prediction iteration iterate newton method convergence behavior localization method...
Proceedings Papers
Intelligent Time-Stepping for Practical Numerical Simulation
Available to PurchasePublisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Simulation Conference, October 26, 2021
Paper Number: SPE-204002-MS
... Abstract We present a novel reservoir simulator time-step selection approach which uses machine-learning (ML) techniques to analyze the mathematical and physical state of the system and predict time-step sizes which are large while still being efficient to solve, thus making the simulation...
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