Mechanical Engineering and Materials Science
Date of Award
Doctor of Philosophy (PhD)
Chair and Committee
Ramesh K Agarwal
With heightened concerns on CO2 emissions from pulverized-coal power plants, there has been major emphasis in recent years on the development of safe and economical Geological Carbon Sequestration: GCS) technology. Although among one of the most promising technologies to address the problem of anthropogenic global-warming due to CO2 emissions, the detailed mechanisms of GCS are not well-understood. As a result, there remain many uncertainties in determining the sequestration capacity of the formation/reservoir and the safety of sequestered CO2 due to leakage. These uncertainties arise due to lack of information about the detailed interior geometry of the formation and the heterogeneity in its geological properties such as permeability and porosity which influence the sequestration capacity and plume migration. Furthermore, the sequestration efficiency is highly dependent on the injection strategy which includes injection rate, injection pressure, type of injection well employed and its orientation etc. The goal of GCS is to maximize the sequestration capacity and minimize the plume migration by optimizing the GCS operation before proceeding with its large scale deployment.
In this dissertation, numerical simulations of GCS are conducted using the DOE multi-phase flow solver TOUGH2: Transport of Unsaturated Groundwater and Heat). A multi-objective optimization code based on genetic algorithm is developed to optimize the GCS operation for a given geological formation. Most of the studies are conducted for
sequestration in a saline formation: aquifer). First, large scale GCS studies are conducted for three identified saline formations for which some experimental data and computations performed by other investigators are available, namely the Mt. Simon formation in Illinois basin, Frio formation in southwest Texas, and the Utsira formation off the coast of Norway. These simulation studies have provided important insights as to the key sources of uncertainties that can influence the accuracy in simulations. For optimization of GCS practice, a genetic algorithm: GA) based optimizer has been developed and combined with TOUGH2. Designated as GA-TOUGH2, this combined solver/optimizer has been validated by performing optimization studies on a number of model problems and comparing the results with brute force optimization which requires large number of simulations. Using GA-TOUGH2, an innovative reservoir engineering technique known as water-alternating-gas: WAG) injection is investigated in the context of GCS; GA-TOUGH2 is applied to determine the optimal WAG operation for enhanced CO2 sequestration capacity. GA-TOUGH2 is also used to perform optimization designs of time-dependent injection rate for optimal injection pressure management, and optimization designs of well distribution for minimum well interference. Results obtained from these optimization designs suggest that over 20% reduction of in situ CO2 footprint, greatly enhanced CO2 dissolution, and significantly improved well injectivity can be achieved by employing GA-TOUGH2. GA-TOUGH2 has also been employed to determine the optimal well placement in a multi-well injection operation. GA-TOUGH2 appears to hold great promise in studying a host of other optimization problems related to GCS.
Zhang, Zheming, "Numerical Simulation and Optimization of CO2 Sequestration in Saline Aquifers" (2013). All Theses and Dissertations (ETDs). 1097.