Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting

• URL: http://arxiv.org/abs/2209.11885v1
• Date: Fri, 23 Sep 2022 23:28:40 GMT
• Title: Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting
• Authors: Wendi Liu, Michael J. Pyrcz
• Abstract summary: Production forecast based on historical data provides essential value for developing hydrocarbon resources. We propose a grid-free, physics-informed graph neural network (PI-GNN) for production forecasting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Production forecast based on historical data provides essential value for developing hydrocarbon resources. Classic history matching workflow is often computationally intense and geometry-dependent. Analytical data-driven models like decline curve analysis (DCA) and capacitance resistance models (CRM) provide a grid-free solution with a relatively simple model capable of integrating some degree of physics constraints. However, the analytical solution may ignore subsurface geometries and is appropriate only for specific flow regimes and otherwise may violate physics conditions resulting in degraded model prediction accuracy. Machine learning-based predictive model for time series provides non-parametric, assumption-free solutions for production forecasting, but are prone to model overfit due to training data sparsity; therefore may be accurate over short prediction time intervals. We propose a grid-free, physics-informed graph neural network (PI-GNN) for production forecasting. A customized graph convolution layer aggregates neighborhood information from historical data and has the flexibility to integrate domain expertise into the data-driven model. The proposed method relaxes the dependence on close-form solutions like CRM and honors the given physics-based constraints. Our proposed method is robust, with improved performance and model interpretability relative to the conventional CRM and GNN baseline without physics constraints.

Related papers

• Real-Time Performance Analysis of Multi-Fidelity Residual Physics-Informed Neural Process-Based State Estimation for Robotic Systems [0.0]
  We present a novel real-time, data-driven estimation approach based on the multi-fidelity residual physics-informed neural process (MFR-PINP)<n>Specifically, we address the model-mismatch issue of selecting an accurate kinematic model by tasking the MFR-PINP to also learn the residuals between simple, low-fidelity predictions and complex, high-fidelity ground-truth dynamics.<n>We provide implementation details of our MFR-PINP-based estimator for a hybrid online learning setting to validate our model's usage in real-time applications.
  arXiv  Detail & Related papers  (2025-11-11T13:30:51Z)
• An End-to-End Differentiable, Graph Neural Network-Embedded Pore Network Model for Permeability Prediction [0.42970700836450487]
  Pore network models (PNMs) estimate pore-scale hydraulic conductance, limiting their accuracy in complex structures.<n>We present an end-to-end differentiable hybrid framework that embeds a graph neural network (GNN) into a PNM.<n>The model achieves high accuracy and adjoins well across different scales, outperforming both pure data-driven and traditional PNM approaches.
  arXiv  Detail & Related papers  (2025-09-17T09:15:23Z)
• Hybrid Adaptive Modeling using Neural Networks Trained with Nonlinear Dynamics Based Features [5.652228574188242]
  This paper introduces a novel approach that departs from standard techniques by uncovering information from nonlinear dynamical modeling and embedding it in data-based models. By explicitly incorporating nonlinear dynamic phenomena through perturbation methods, the predictive capabilities are more realistic and insightful compared to knowledge obtained from brute-force numerical simulations.
  arXiv  Detail & Related papers  (2025-01-21T02:38:28Z)
• Bayesian Model Parameter Learning in Linear Inverse Problems: Application in EEG Focal Source Imaging [49.1574468325115]
  Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly.<n>We studied a linear inverse problem that included an unknown non-linear model parameter.<n>We utilized a Bayesian model-based learning approach that allowed signal recovery and subsequently estimation of the model parameter.
  arXiv  Detail & Related papers  (2025-01-07T18:14:24Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Towards Physically Consistent Deep Learning For Climate Model Parameterizations [46.07009109585047]
  parameterizations are a major source of systematic errors and large uncertainties in climate projections. Deep learning (DL)-based parameterizations, trained on data from computationally expensive short, high-resolution simulations, have shown great promise for improving climate models. We propose an efficient supervised learning framework for DL-based parameterizations that leads to physically consistent models.
  arXiv  Detail & Related papers  (2024-06-06T10:02:49Z)
• Online model error correction with neural networks: application to the Integrated Forecasting System [0.27930367518472443]
  We develop a model error correction for the European Centre for Medium-Range Weather Forecasts using a neural network. The network is pre-trained offline using a large dataset of operational analyses and analysis increments. It is then integrated into the IFS within the Object-Oriented Prediction System (OOPS) so as to be used in data assimilation and forecast experiments.
  arXiv  Detail & Related papers  (2024-03-06T13:36:31Z)
• Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures [6.483318568088176]
  PiML method integrates scientific principles and physical laws into deep neural networks to model seismic responses of nonlinear structures. Manipulating the equation of motion helps learn system nonlinearities and confines solutions within physically interpretable results. Result handles complex data better than existing physics-guided LSTM models and outperforms other non-physics data-driven networks.
  arXiv  Detail & Related papers  (2024-02-28T02:16:03Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Kalman Filter for Online Classification of Non-Stationary Data [101.26838049872651]
  In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps. We introduce a probabilistic Bayesian online learning model by using a neural representation and a state space model over the linear predictor weights. In experiments in multi-class classification we demonstrate the predictive ability of the model and its flexibility to capture non-stationarity.
  arXiv  Detail & Related papers  (2023-06-14T11:41:42Z)
• Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions [12.336689498639366]
  We propose a novel Dynamic Graph Neural Network with Adaptive Edge Attributes (DGN-AEA) on the message passing network. Unlike prior information to establish edges, our method can obtain adaptive edge information through end-to-end training without any prior information.
  arXiv  Detail & Related papers  (2023-02-20T13:45:55Z)
• Strategic Geosteeering Workflow with Uncertainty Quantification and Deep Learning: A Case Study on the Goliat Field [0.0]
  This paper presents a practical workflow consisting of offline and online phases. The offline phase includes training and building of an uncertain prior near-well geo-model. The online phase uses the flexible iterative ensemble smoother (FlexIES) to perform real-time assimilation of extra-deep electromagnetic data.
  arXiv  Detail & Related papers  (2022-10-27T15:38:26Z)
• Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network [0.0]
  Fast updates based on real-time data are essential when drilling in complex reservoirs with high uncertainties in pre-drill models. We propose a generative adversarial deep neural network (GAN) trained to reproduce geologically consistent 2D sections of fluvial successions. In our example, the method reduces uncertainty and correctly predicts most major geological features up to 500 meters ahead of drill-bit.
  arXiv  Detail & Related papers  (2022-07-04T12:52:38Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.