Qirun Fu

PhD Student

PhD student

Active

Location:

Building 5, Level 0, 0874-WS31

Social Profile:

Biography

Combined with the ML/DL approach, my research interest focuses on surrogate modeling to simulate multi-phase fluid flows in energy and petroleum related fields, especially in carbon sequestration and unconventional energy.

Research Interests

The main research interest lies in combining advanced machine learning or deep learning techniques with energy-related surrogate modeling to simulate subsurface multi-phase flow response as accurately and rapidly as possible, especially for carbon sequestration and unconventional energy sources. Moreover, a focus is also placed on the construction of large-scale surrogate models, as well as the integration of surrogate models with optimization algorithms, history matching, and other aspects.

Selected Publications

Predicting CO2-EOR and storage in low-permeability reservoirs with deep learning-based surrogate flow models

Meng, S., Fu, Q., Tao, J., Liang, L., & Xu, J.

Geoenergy Science and Engineering, 233, 212467, (2024)

A novel deep learning-based automatic search workflow for CO2 sequestration surrogate flow models

Xu, J., Fu, Q., & Li, H.

Fuel, 354, 129353, (2023)

 

Education

  • M.S. Offshore oil and gas engineering, China University of Petroleum (East China), China, 2024.
  • B.S. Offshore oil and gas engineering, China University of Petroleum (East China), China, 2021.

Professional Profile

  • 2024-present, Ph.D. Student, KAUST, Thuwal, Saudi Arabia

KAUST Affiliations

  • Physical Science and Engineering Division (PSE)

Research Interests Keywords

Surrogate modeling Carbon Storage Numerical Simulation ML DL