Digital Twin Predicting Non-Linear Well Dynamics

The patent combines a physics-based model with a machine learning model into a hybrid system. The physics-based model provides general dynamics prediction using lumped-parameter approach, while the ML model specifically handles complex non-linear behaviors that the physics model cannot capture. The two models work together to compensate for each other's limitations, achieving both accuracy and adaptability.

The patent creates a composite modeling approach by integrating two different modeling paradigms: traditional physics-based modeling and modern machine learning modeling. This composite model leverages the strengths of both approaches - the physical interpretability and general accuracy of physics-based models, and the adaptive pattern recognition capability of ML models - to achieve superior overall performance in predicting well dynamics.

The patent implements a feedback mechanism where the physics-based model provides constraints and guidance to the machine learning model during training and prediction. The physics model ensures that predictions remain physically plausible, preventing the ML model from overfitting to noise or unrealistic patterns in the training data. This feedback loop maintains generalization capability while improving real-time prediction accuracy.

The patent modifies the training approach by incorporating physics-based constraints as additional parameters or regularization terms in the ML model training process. This changes the optimization landscape to favor solutions that are both data-driven and physically consistent, thereby improving real-time accuracy without sacrificing generalization to new operating conditions.