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

Multi-Context Interaction Network for Few-Shot Segmentation

Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies. Due to the huge difference between support and query samples, most existing approaches focus on extracting high-level representations of the same layers for support-query correlations but neglect the shift issue between different layers and scales. In this paper, we propose a Multi-Context Interaction Network (MCINet) to remedy this issue by fully exploiting and interacting with the multi-scale contextual information contained in the support-query pairs. Specifically, MCINet improves FSS from the perspectives of boosting the query representations by incorporating the low-level structural information from another query branch into the high-level semantic features, enhancing the support-query correlations by exploiting both the same-layer and adjacent-layer features, and refining the predicted results by a multi-scale mask prediction strategy, with which the different scale contents have bidirectionally interacted. Experiments on two benchmarks demonstrate that our approach reaches SOTA performances and outperforms the best competitors with many desirable advantages, especially on the challenging COCO dataset

Economics of Carbon Dioxide Sequestration versus a Suite of Alternative Renewable Energy Sources for Electricity Generation in U.S., California and Illinois

An equilibrium economic model for policy evaluation related to electricity generation at national and individual state level in U.S has been developed. The model takes into account the non-renewable and renewable energy sources, demand and supply factors and environmental constraints (CO2 emissions). Economic policy analysis experiments are carried out to determine the consequences of switching the sources of electricity generation under two scenarios:  in first scenario, a switch from coal to renewable sources is made for 10% of electricity generation; in the second scenario, the switch is made for 10% of electricity generation from coal to coal with clean coal technology by employing CO2 capture and sequestration (CCS). The cost of electricity generation from various non-renewable and renewable sources is different and is taken into account in the model. The consequences of this switch on supply and demand, employment, wages, and emissions are obtained from the economic model under three scenarios: (1) energy prices are fully regulated, (2) energy prices are fully adjusted with electricity supply fixed, and (3) energy prices and electricity supply both are fully adjusted. The model is applied to the states of California and Illinois, and at national level. Keywords: Carbon dioxide sequestration and mitigation; Electricity generation; Renewable energy; State-level analysis JEL Classifications: C54; C68; Q42; Q4

Economics of Carbon Dioxide Sequestration and Mitigation versus a Suite of Alternative Renewable Energy Sources for Electricity Generation in U.S.

An equilibrium economic model for policy evaluation related to electricity generation in U.S has been developed; the model takes into account the non-renewable and renewable energy sources, demand and supply factors and environmental constraints. The non-renewable energy sources include three types of fossil fuels: coal, natural gas and petroleum, and renewable energy sources include nuclear, hydraulic, wind, solar photovoltaic, biomass wood, biomass waste and geothermal. Energy demand sectors include households, industrial manufacturing and non-manufacturing commercial enterprises. Energy supply takes into account the electricity delivered to the consumer by the utility companies at a certain price which maybe different for retail and wholesale customers. Environmental risks primarily take into account the CO2 generation from fossil fuels. The model takes into account the employment in various sectors and labor supply and demand. Detailed electricity supply and demand data, electricity cost data, employment data in various sectors and CO2 generation data are collected for a period of nineteen years from 1990 to 2009 in U.S. The model is employed for policy analysis experiments if a switch is made in sources of electricity generation, namely from fossil fuels to renewable energy sources. As an example, we consider a switch of 10% of electricity generation from coal to 5% from wind, 3% from solar photovoltaic, 1% from biomass wood and 1% from biomass waste. The model is also applied to a switch from 10% coal to 10% from clean coal technologies. It should be noted that the cost of electricity generation from different sources is different and is taken into account. The consequences of this switch on supply and demand, employment, wages, and emissions are obtained from the economic model under three scenarios: (1) energy prices are fully regulated, (2) energy prices are fully adjusted with electricity supply fixed, and (3) energy prices and electricity supply both are fully adjusted. Keywords: Carbon dioxide sequestration and mitigation; Renewable energy; Electricity generation JEL Classifications: C54; C68; Q42; Q4

Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained

Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained