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

From Pixels to Prose: A Large Dataset of Dense Image Captions

Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To bridge this gap, we introduce PixelProse, a comprehensive dataset of over 16M (million) synthetically generated captions, leveraging cutting-edge vision-language models for detailed and accurate descriptions. To ensure data integrity, we rigorously analyze our dataset for problematic content, including child sexual abuse material (CSAM), personally identifiable information (PII), and toxicity. We also provide valuable metadata such as watermark presence and aesthetic scores, aiding in further dataset filtering. We hope PixelProse will be a valuable resource for future vision-language research. PixelProse is available at https://huggingface.co/datasets/tomg-group-umd/pixelproseComment: pixelprose 16M datase

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

Pruning for Efficient Deep Learning: From CNNs to Generative Models

Deep learning models have shown remarkable success in visual recognition and generative modeling tasks in computer vision in the last decade. A general trend is that their performance improves with an increase in the size of their training data, model capacity, and training iterations on modern hardware. However, the increase in model size naturally leads to higher computational complexity and memory footprint, thereby necessitating high-end hardware for their deployment. This trade-off prevents the deployment of deep learning models in resource-constrained environments such as robotic applications, mobile phones, and edge devices employed in the Artificial Internet of Things (AIoT). In addition, private companies and organizations have to spend significant resources on cloud services to serve deep models for their customers. In this dissertation, we develop model pruning and Neural Architecture Search (NAS) methods to improve the inference efficiency of deep learning models for visual recognition and generative modeling applications. We design our methods to be tailored to the unique characteristics of each model and its task. In the first part, we present model pruning and efficient NAS methods for Convolutional Neural Network (CNN) classifiers. We start by proposing a pruning method that leverages interpretations of a pretrained model's decisions to prune its redundant structures. Then, we provide an efficient NAS method to learn kernel sizes of a CNN model using their training dataset and given a parameter budget for the model, enabling designing efficient CNNs customized for their target application. Finally, we develop a framework for simultaneous pretraining and pruning of CNNs, which combines the first two stage of the pretrain-prune-finetune pipeline commonly used in model pruning and reduces its complexity. In the second part, we propose model pruning methods for visual generative models. First, we present a pruning method for conditional Generative Adversarial Networks (GANs) in which we prune the generator and discriminator models in a collaborative manner. We then address the inference efficiency of diffusion models by proposing a method that prunes a pretrained diffusion model into a mixture of efficient experts, each handling a separate part of the denoising process. Finally, we develop an adaptive prompt-tailored pruning method for modern text-to-image diffusion models. It prunes a pretrained model like Stable Diffusion into a mixture of efficient experts such that each expert specializes in certain type of input prompts

Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment

Structural model pruning is a prominent approach used for reducing the computational cost of Convolutional Neural Networks (CNNs) before their deployment on resource-constrained devices. Yet, the majority of proposed ideas require a pretrained model before pruning, which is costly to secure. In this paper, we propose a novel structural pruning approach to jointly learn the weights and structurally prune architectures of CNN models. The core element of our method is a Reinforcement Learning (RL) agent whose actions determine the pruning ratios of the CNN model's layers, and the resulting model's accuracy serves as its reward. We conduct the joint training and pruning by iteratively training the model's weights and the agent's policy, and we regularize the model's weights to align with the selected structure by the agent. The evolving model's weights result in a dynamic reward function for the agent, which prevents using prominent episodic RL methods with stationary environment assumption for our purpose. We address this challenge by designing a mechanism to model the complex changing dynamics of the reward function and provide a representation of it to the RL agent. To do so, we take a learnable embedding for each training epoch and employ a recurrent model to calculate a representation of the changing environment. We train the recurrent model and embeddings using a decoder model to reconstruct observed rewards. Such a design empowers our agent to effectively leverage episodic observations along with the environment representations to learn a proper policy to determine performant sub-networks of the CNN model. Our extensive experiments on CIFAR-10 and ImageNet using ResNets and MobileNets demonstrate the effectiveness of our method.Comment: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 202

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

Bureau of Economic Geolog