Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls

• URL: http://arxiv.org/abs/2310.06319v1
• Date: Tue, 10 Oct 2023 05:29:33 GMT
• Title: Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
• Authors: Jungang Chen, Eduardo Gildin, John E. Killough
• Abstract summary: A physics-informed convolutional neural network is proposed to simulate two phase flow in porous media. finite volume scheme is adopted to discretize flow equations. N Neumann boundary conditions are seamlessly incorporated into the semi-discretized equations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A physics-informed convolutional neural network is proposed to simulate two phase flow in porous media with time-varying well controls. While most of PICNNs in existing literatures worked on parameter-to-state mapping, our proposed network parameterizes the solution with time-varying controls to establish a control-to-state regression. Firstly, finite volume scheme is adopted to discretize flow equations and formulate loss function that respects mass conservation laws. Neumann boundary conditions are seamlessly incorporated into the semi-discretized equations so no additional loss term is needed. The network architecture comprises two parallel U-Net structures, with network inputs being well controls and outputs being the system states. To capture the time-dependent relationship between inputs and outputs, the network is well designed to mimic discretized state space equations. We train the network progressively for every timestep, enabling it to simultaneously predict oil pressure and water saturation at each timestep. After training the network for one timestep, we leverage transfer learning techniques to expedite the training process for subsequent timestep. The proposed model is used to simulate oil-water porous flow scenarios with varying reservoir gridblocks and aspects including computation efficiency and accuracy are compared against corresponding numerical approaches. The results underscore the potential of PICNN in effectively simulating systems with numerous grid blocks, as computation time does not scale with model dimensionality. We assess the temporal error using 10 different testing controls with variation in magnitude and another 10 with higher alternation frequency with proposed control-to-state architecture. Our observations suggest the need for a more robust and reliable model when dealing with controls that exhibit significant variations in magnitude or frequency.

Related papers

• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• Physics-informed Convolutional Recurrent Surrogate Model for Reservoir Simulation with Well Controls [0.0]
  This paper presents a novel surrogate model for modeling subsurface fluid flow with well controls using a physics-informed convolutional neural (RNN) The model uses a convolutional long-term memory (ConvLSTM) to capture the recurrent dependencies of the state evolution dynamics in porous flow. The proposed model provides a new approach for efficient and accurate prediction of subsurface fluid flow, with potential applications in optimal control design for reservoir engineering.
  arXiv  Detail & Related papers  (2023-05-15T22:43:18Z)
• Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design [68.86220939532373]
  The finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. The proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm.
  arXiv  Detail & Related papers  (2022-07-19T16:37:24Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)
• Physics-Informed Neural Nets-based Control [5.252190504926357]
  This work presents a new framework called Physics-Informed Neural Nets-based Control (PINC) PINC is amenable to control problems and able to simulate for longer-range time horizons that are not fixed beforehand. We showcase our method in the control of two nonlinear dynamic systems.
  arXiv  Detail & Related papers  (2021-04-06T14:55:23Z)
• Echo State Network for two-dimensional turbulent moist Rayleigh-B\'enard convection [0.0]
  We apply an echo state network to approximate the evolution of moist Rayleigh-B'enard convection. We conclude that our model is capable of learning complex dynamics.
  arXiv  Detail & Related papers  (2021-01-27T11:27:16Z)
• Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers [5.6385744392820465]
  We develop a hierarchy of deep neural network time-steppers to approximate the flow map of the dynamical system over a disparate range of time-scales. The resulting model is purely data-driven and leverages features of the multiscale dynamics. We benchmark our algorithm against state-of-the-art methods, such as LSTM, reservoir computing, and clockwork RNN.
  arXiv  Detail & Related papers  (2020-08-22T07:16:53Z)
• Fast Modeling and Understanding Fluid Dynamics Systems with Encoder-Decoder Networks [0.0]
  We show that an accurate deep-learning-based proxy model can be taught efficiently by a finite-volume-based simulator. Compared to traditional simulation, the proposed deep learning approach enables much faster forward computation. We quantify the sensitivity of the deep learning model to key physical parameters and hence demonstrate that the inversion problems can be solved with great acceleration.
  arXiv  Detail & Related papers  (2020-06-09T17:14:08Z)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)

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.