Physics Informed Deep Learning for Flow and Transport in Porous Media

Understand

We present our progress on the application of physics informed deep learning to reservoir simulation problems.

• The model is a neural network that is jointly trained to respect governing physical laws and match boundary conditions.
• The methodology is hereby used to simulate a 2-phase immiscible transport problem (Buckley-Leverett).
• The model is able to produce an accurate physical solution both in terms of shock and rarefaction and honors the governing partial differential equation along with initial and boundary conditions.