A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media
Researchers have developed a novel machine learning framework to improve the modeling of gas flow in porous media. This approach combines a Klinkenberg-enhanced constitutive relation with a Hopf-Cole transformation to linearize the governing equations. A shared-trunk neural network architecture and a Deep Least-Squares solver are used for accurate prediction of pressure and velocity fields, also enabling inverse modeling for parameter estimation. AI
IMPACT This framework offers a more accurate and computationally efficient method for simulating gas transport and estimating flow properties in challenging geological formations.