Core Concepts
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.
Abstract
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.
Stats
"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"