Integration of reservoir simulation with deep learning for subsurface reservoir management


Project Description

DSFT is a research team with diverse expertise including numerical modeling, machine/deep learning and energy system management. We are dedicating to technology development of advanced physics-driven numerical simulation and data-driven modeling for fluid flow in porous media. The goal of this visiting student project is to develop reservoir models to simulate the process of geological carbon storage, geothermal recovery or hydrogen storage, and ultimately use deep learning (e.g., convolutional/recurrent/graphic neural networks) or physics-informed neural networks to establish surrogate models for fast prediction of these nonlinear processes, and ultimately be ready for application of uncertainty quantification and also optimization. We seek for self-motivated, dedicated and creative students who wants to address challenging energy and environmental related engineering problems, whose majors are from petroleum engineering, computational mathematics, machine learning or closely related fields. Desired qualification will be competitive students with good skills of reservoir simulation, python or Julia programming and deep learning.
Program - Energy Resources and Petroleum Engineering
Division - Physical Sciences and Engineering
Center Affiliation - Ali I. Al-Naimi Petroleum Engineering Research Center
Field of Study - Petroleum Engineering, Computational Mathematics, Machine Learning

About the

Bicheng Yan

Assistant Professor, Energy Resources and Petroleum Engineering

Bicheng Yan

Desired Project Deliverables

- report - prototype