报告内容摘要

Geological Carbon Sequestration (GCS) involves the injection of pressurized CO2   over 1000 m underground, to reduce atmospheric carbon.  Accurate estimation of CO2 plume development and pressure response can greatly enhance the safety of geological carbon sequestration (GCS) by indicating where to search for possible breaches in the integrity of the system, which might cause environmental damage.  Unfortunately few monitoring sites are feasible. Our goal is to demonstrate an efficient computational process by which a combination of reservoir modeling, optimization, and uncertainty analysis can assess alternative sparse monitoring plans in terms of their ability to give an accurate estimate of current plume and forecast future plumes. The example application mimics a CO2 sequestration pilot test at Frio with saline aquifer injection. The TOUGH2 multiphase simulator takes 2 hours/simulation. We developed a method for lumping parameters, which is essential since the limited number of monitoring wells means that relatively few parameters can be estimated from available data.  The plume position could be determined with an average correlation coefficient of up to R?=0.92 (for current plume) and R?=0.8 (for plume forecasted 5 years in future). These results were obtained using only pressure measurements, two monitoring wells, and a modest number of simulations. The speaker will also briefly describe two current energy-related studies using optimization a) for controlling hydropower and wind production at the 17 reservoir BPA system and b) for estimating parameters for the CLM 4.5, a major module in global climate models.

报告人简介

Prof. Shoemaker is a member of the National Academy of Engineering and is a Fellow in AGU, SIAM, INFORMS and Distinguished Member of ASCE. Prof. Shoemaker's research focuses on finding costeffective, robust solutions for environmental problems by using optimization, modeling and statistical analyses. This includes development of general purpose, numerically efficient nonlinear and global optimization algorithms utilizing high performance computing (including asynchronous parallelism) and applications to data on complex, nonlinear environmental systems. Her algorithms address local and global continuous and integer optimization, stochastic optimal control, and uncertainty quantification problems. In her recent research algorithm efficiency is improved with the use of surrogate response surfaces iteratively built during the research process and with intelligent algorithms that effectively utilize parallel and distributed computing. Her applications areas include physical and biological groundwater remediation, carbon sequestration, pesticide management, ecology, and calibration of climate and watershed models.