Modelling the Eddy Pattern in the harbour of Zeebrugge

Hydrodynamic

Numerical Simulation and Optimization of Carbon Dioxide Utilization and Storage in Enhanced Gas Recovery and Enhanced Geothermal Systems

With rising concerns surrounding CO2 emissions from fossil fuel power plants, there has been a strong emphasis on the development of safe and economical Carbon Capture Utilization and Storage (CCUS) technology. Two methods that show the most promise are Enhanced Gas Recovery (EGR) and Enhanced Geothermal Systems (EGS). In Enhanced Gas Recovery a depleted or depleting natural gas reservoir is re-energized with high pressure CO2 to increase the recovery factor of the gas. As an additional benefit following the extraction of natural gas, the reservoir would serve as a long-term storage vessel for the captured carbon. CO2 based Enhanced Geothermal Systems seek to increase the heat extracted from a given geothermal reservoir by using CO2 as a working fluid. Carbon sequestration is accomplished as a result of fluid losses throughout the life of the geothermal system. Although these technologies are encouraging approaches to help in the mitigation of anthropogenic CO2 emissions, the detailed mechanisms involved are not fully understood. There remain uncertainties in the efficiency of the systems over time, and the safety of the sequestered CO2 due to leakage. In addition, the efficiency of both natural gas extraction in EGR and heat extraction in EGS are highly dependent on the injection rate and injection pressure. Before large scale deployment of these technologies, it is important to maximize the extraction efficiency and sequestration capacity by optimizing the injection parameters. In this thesis, numerical simulations of subsurface flow in EGR and EGS are conducted using the DOE multiphase flow solver TOUGH2 (Transport of Unsaturated Groundwater and Heat). A previously developed multi-objective optimization code based on a genetic algorithm is modified for applications to EGR and EGS. For EGR study, a model problem based on a benchmark-study that compares various mathematical and numerical models for CO2 storage is considered. For EGS study a model problem based on previous studies (with parameters corresponding to the European EGS site at Soultz) is considered. The simulation results compare well with the computations of other investigators and give insight into the parameters that can influence the simulation accuracy. Optimizations for EGR and EGS problems are carried out with a genetic algorithm (GA) based optimizer combined with TOUGH2, designated as GA-TOUGH2. Validation of the optimizer was achieved by comparison of GA based optimization studies with the brute-force run of large number of simulations. Using GA-TOUGH2, optimal time-independent and time-dependent injection profiles were determined for both EGR and EGS. Optimization of EGR problem resulted in a larger natural gas production rate, a shorter total operation time, and an injection pressure well below the fracture pressure. Optimization of EGS problem resulted in a precise management of the production temperature profile, heat extraction for the entire well life, and more efficient utilization of CO2. The results of these studies will hopefully pave the way for future GA-TOUGH2 based optimization studies to improve the modeling of CCUS projects

Wind-solar-hydrothermal dispatch using convex optimization

In this research a convex optimization methodology is proposed for the Shortterm hydrothermal scheduling (STHS). In addition, wind and solar generation are also considered under a robust approach by modeling the equilibrium of power flow constraint as chance box constraints, which allows determining the amount of renewable source available with a specific probability value. The proposed methodology guarantees global optimum of the convexified model andfast convergences..

Short-term generation scheduling in a hydrothermal power system.

SIGLEAvailable from British Library Document Supply Centre- DSC:D173872 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Numerical Simulation and Optimization of CO2 Sequestration in Saline Aquifers

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 xvi 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