Invited Speaker

Abstract: This study applies an efficient optimization method based on a multivariate adaptive regression spline (MARS) technique to determine the optimal design and engineering of production operation at a potential geothermal reservoir. The faster MARS-based statistical model is used as a surrogate for higher-fidelity physical models during computationally intensive optimization process. Its use allows for the exploration of the impacts of specific engineering design parameters in the context of geologic uncertainty as a means to both understand and maximize profitability of the production operation. The MARS model is developed from a training dataset generated by a finite set of computationally complex hydrothermal models applied to the reservoir. Its application reveals that the optimal engineering design variables can differ considerably assuming different choices of hydrothermal flow properties, which, in turn, indicates the importance of reducing the uncertainty of key geologic properties. The major uncertainty sources in the natural-system are identified and ranked first by an efficient MARS-enabled sensitivity quantification, which is then used to assist evaluating the effect of geological uncertainties on optimized results. The parameter sensitivity analysis suggests that groundwater circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method during geothermal extraction. Reservoir histories simulated using optimal parameters with different constraints are analysed and compared to investigate the longevity and maximum profit of the geothermal resources. The comparison shows that the longevity and profit are very likely to be overestimated by optimizations without appropriate constraints on natural conditions. In addition to geothermal energy production, this optimization approach can also be used to manage other geologic resource operations, such as fossil energy production or CO2 sequestration, under uncertain reservoir conditions.

Biography: 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.