Dr. Mingjie Chen
Senior Hydrogeologist, Water Research Center, Sultan Qaboos University, Oman
Dr. Mingjie Chen holds a B.E. in Environmental Engineering (1997) from Tsinghua University (China), a M.Sc. in Environmental Sciences (2000) from Peking University (China) and a Ph.D degree in Hydrogeology (2005) from University of California, Santa Barbara (USA). After 10 years of research experiences in Los Alamos National Lab, Tufts University, Lawrence Livermore National Lab in USA, Dr. Chen joined Water Research Center, Sultan Qaboos University (Oman) in 2014 as a senior hydrogeologist. Dr. Chen’ research focuses on using laboratory experiments, mathematical models and numerical techniques to study multiple fluids (water, oil, gas) flow and contaminant transport in subsurface area. He has conducted more than 20 research projects on underground environment remediation, hydrocarbon reservoirs, CO₂ utilization and sequestration, geothermal reservoir, groundwater modeling and management. At present, Dr. Chen serves as the Associate Editor for Arabian Journal of Geosciences, Hydrogeology Journal, and Journal of Hydrology.

Speech Title: Efficient calibration of a CO₂ sequestration model by using a surrogate-based framework method

Abstract: Numerical model calibrations usually incur a large number of model runs, demanding tremendous computing efforts if the single hydrogeological model simulation is time-consuming. One way to address this challenge is to surrogate the expensive physical model with fast statistical model during calibration. The surrogate model, which can generate fast solutions, is fitted and validated using training data obtained from the associated physical model. In this study, Bagging MARS (BMARS), an upgrade from Multivariate Adaptive Regression Splines (MARS) algorithm, is used to construct the surrogate of a three-dimensional CO₂ sequestration model, which is developed to simulate CO₂ injection and migration in a fault-compartmentalized reservoir. The BMARS surrogate model is then used in model calibration to estimate specified reservoir model input parameters efficiently. The results demonstrate that the BMARS model can improve fitting stability and predictive accuracy against the ordinary MARS model. Parameter sensitivity analysis, which is efficiently conducted using the BMARS model, suggest that permeability of Fault#10 and caprock dominate the pressure buildup in this fault-compartmentalized reservoir. Hence priority should be given to investment in estimating these two reservoir properties. Overpressure propagation and CO₂ migration in the reservoir responding to three years of CO₂ injection are also analyzed using the calibrated model. The calibrated CO₂ sequestration model provides a useful tool to evaluate the future operation and risk assessment of the reservoir.