Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells

Centrala begrepp

The author argues that Physics-Informed Neural Networks (PINNs) with skip connections enhance modeling accuracy for gas-lifted oil wells, improving gradient flow and control predictions.

Sammanfattning

Physics-Informed Neural Networks (PINNs) with skip connections are proposed to model and control gas-lifted oil wells. The approach incorporates physics laws into the loss function, leading to more accurate gradients during training. The study demonstrates superior performance in reducing prediction errors and enhancing gradient flow through network layers. Additionally, Model Predictive Control (MPC) is effectively applied for regulating bottom-hole pressure even in the presence of noisy measurements.
Applications of PINNs extend to various engineering areas, including fluid dynamics, multi-body dynamics, and generative adversarial networks. The study highlights the importance of addressing nonlinear terms in ODEs for effective training of PINC networks. Improved architectures with skip connections are shown to mitigate gradient pathologies and enhance training efficiency.
The hierarchical architecture proposed involves two modules: one for predicting states using PINC and another for predicting algebraic variables like bottom-hole pressure using a feedforward neural network. This setup streamlines additional variable predictions without retraining the main network.
Overall, the study emphasizes the significance of incorporating physics laws into neural networks for accurate modeling and control applications in complex systems like gas-lifted oil wells.

Statistik

"reducing the validation prediction error by an average of 67%"
"increasing its magnitude by four orders of magnitude compared to the original PINC"
"Model Predictive Control (MPC) in regulating the bottom-hole pressure"

Citat

Viktiga insikter från

Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells

by Jonas Ekelan... på arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.02289.pdf

Djupare frågor

How can incorporating physics laws into neural networks impact other engineering applications

Incorporating physics laws into neural networks can have a significant impact on various engineering applications. By integrating the fundamental principles and constraints of the physical system into the network's training process, the resulting model becomes more interpretable and generalizable. This approach not only enhances the accuracy and reliability of predictions but also provides insights into how different variables interact within the system. In engineering applications such as fluid dynamics, structural analysis, or control systems, physics-informed neural networks can offer a more robust framework for modeling complex behaviors and making informed decisions based on underlying physical laws.

What challenges may arise when dealing with highly nonlinear terms in ODEs during training

Dealing with highly nonlinear terms in Ordinary Differential Equations (ODEs) during training poses several challenges. Nonlinear terms introduce complexities that may lead to gradient vanishing or exploding problems, hindering the convergence of neural network models. These nonlinearity issues can make it difficult to optimize loss functions effectively, impacting both training stability and prediction accuracy. Additionally, highly nonlinear terms may introduce discontinuities or singularities in mathematical functions, leading to numerical instability during computation. Addressing these challenges requires careful preprocessing of data inputs, regularization techniques to stabilize training processes, and architectural modifications like skip connections to improve gradient flow through network layers.

How can skip connections improve gradient flow in neural networks beyond oil well modeling

Skip connections play a crucial role in improving gradient flow in neural networks beyond oil well modeling by facilitating smoother optimization processes and mitigating common issues like vanishing gradients. By creating direct paths for gradients to flow back through multiple layers without being diminished significantly, skip connections enable better information propagation during training. This helps alleviate optimization difficulties associated with deep architectures by providing shortcuts for gradient updates across different network depths. As a result, skip connections enhance learning efficiency, promote faster convergence rates, and contribute to overall model performance by reducing the likelihood of getting stuck in local minima during optimization tasks.