Integrating carbon capture and storage with energy production from saline aquifers

textTechnologies considered for separating CO₂ from flue gas and injecting CO₂ into saline aquifers are energy intensive, costly, and technically challenging. Production of dissolved natural gas and geothermal energy by extraction of aquifer brine has shown the potential of offsetting the cost of CO₂ capture and storage along with other technical and environmental advantages. The key is to recognize inherent value in the energy content of brine in many parts of the world. Dissolved methane in brine and geothermal energy are two of the sources of energy of many aquifers. For example, geopressured-geothermal aquifers of the US Gulf Coast contain sheer volume of hot brine and dissolved methane. For the same reason, the capacity of these geopressured-geothermal aquifers for storage of CO₂ is remarkable. In this study, various reservoir models were developed from data of Texas and Louisiana Gulf Coast saline aquifers. A systematic study was performed to determine the range of uncertainty of the properties and the prospective of energy production from saline aquifers. Two CO₂ injection strategies were proposed for storage of CO₂ based on the results of simulation studies. Injection of CO₂-saturated brine showed several advantages compared to injection of supercritical CO₂. An overall energy analysis was performed on the closed-loop cycles of capture from power plants, storage of CO₂, and production of energy. The level of cost offset of CCS technology by producing energy from target aquifers strongly depends on the applications of the produced energy. The temperature of the produced brine from geopressured-geothermal aquifers is higher than the temperature of amine stripper column. Calculations for the strategy of injecting CO₂-saturated brine show that the amount of extracted thermal energy from geopressured-geothermal aquifers exceeds the amount of heat required for capturing CO₂ by amine scrubbing. In the process of injecting dissolved CO₂, compressors and pumps should run to pressurize the CO₂ and brine to be transported and achieve the required wellhead pressure. The preliminary estimations indicate that the produced methane provides more energy than that required for pressurization. In the regions where the temperature gradient is normal, the temperature of the produced brine may not be high enough for using in the chemical absorption processes. Separation mechanisms driven by pressure difference are the alternatives for chemical absorption processes since the produced methane can be burned for running the compressors and pumps. Membrane process seems to be the leading technology candidate. The preliminary estimations show that the produced power by extracted methane and geothermal energy exceeds the power needed for membranes, compressors, and pumps. Neither storage of greenhouse gases in saline aquifers nor production of methane and/or geothermal energy from these aquifers are profitable. However, designing a closed looped system by combining methods of capture, storage and production may pay off the whole process at least from the energy point of view.Petroleum and Geosystems Engineerin

Solubility trapping as a potential secondary mechanism for CO2 sequestration during enhanced gas recovery by CO2 injection in conventional natural gas reservoirs : an experimental approach

This study aims to experimentally investigate the potential of solubility trapping mechanism in increasing CO2 storage during EGR by CO2 injection and sequestration in conventional natural gas reservoirs. A laboratory core flooding process was carried out to simulate EGR on a sandstone core at 0, 5, 10 wt% NaCl formation water salinity at 1300 psig, 50 °C and 0.3 ml/min injection rate. The results show that CO2 storage capacity was improved significantly when solubility trapping was considered. Lower connate water salinities (0 and 5 wt%) showed higher CO2 solubility from IFT measurements. With 10% connate water salinity, the highest accumulation of the CO2 in the reservoir was realised with about 63% of the total CO2 injected stored; an indication of improved storage capacity. Therefore, solubility trapping can potentially increase the CO2 storage capacity of the gas reservoir by serving as a secondary trapping mechanism in addition to the primary structural and stratigraphic trapping and improving CH4 recovery

Development of an analytical simulation tool for storage capacity estimation of saline aquifers

Bureau of Economic Geolog

EffConv: Efficient Learning of Kernel Sizes for Convolution Layers of CNNs

Determining kernel sizes of a CNN model is a crucial and non-trivial design choice and significantly impacts its performance. The majority of kernel size design methods rely on complex heuristic tricks or leverage neural architecture search that requires extreme computational resources. Thus, learning kernel sizes, using methods such as modeling kernels as a combination of basis functions, jointly with the model weights has been proposed as a workaround. However, previous methods cannot achieve satisfactory results or are inefficient for large-scale datasets. To fill this gap, we design a novel efficient kernel size learning method in which a size predictor model learns to predict optimal kernel sizes for a classifier given a desired number of parameters. It does so in collaboration with a kernel predictor model that predicts the weights of the kernels - given kernel sizes predicted by the size predictor - to minimize the training objective, and both models are trained end-to-end. Our method only needs a small fraction of the training epochs of the original CNN to train these two models and find proper kernel sizes for it. Thus, it offers an efficient and effective solution for the kernel size learning problem. Our extensive experiments on MNIST, CIFAR-10, STL-10, and ImageNet-32 demonstrate that our method can achieve the best training time vs. accuracy trade-off compared to previous kernel size learning methods and significantly outperform them on challenging datasets such as STL-10 and ImageNet-32. Our implementations are available at https://github.com/Alii-Ganjj/EffConv