Effect of reservoir heterogeneity on CO2 flooding in tight oil reservoirs

Carbon dioxide (CO2)-enhanced oil recovery (EOR) has great potential and opportunity for further development, and it is one of the vital carbon capture, utilization, and storage (CCUS) technologies. However, strong heterogeneity is one of the several challenges in developing reservoirs, especially for China’s continental tight oil reserves. This study investigates the effects of heterogeneous porosity and permeability on CO2 flooding evolution in low-permeable tight formation. We simulated CO2-EOR using a numerical model developed on the platform of TOUGH2MP-TMVOC to evaluate the effect of different levels of heterogeneity on oil production, gas storage, and flow behaviors in a tight reservoir, controlled by standard deviation and correlation length. A comparison of nine cases reveals that porosity heterogeneity commonly intensifies flow channeling, and there is an oil production decline with higher standard deviation and longer correlation length of porosity field. In addition, the porosity correlation length has a negligible effect on reservoir performance when the standard deviation is relatively low. Furthermore, strong heterogeneity also has a negative impact on the storage capacity of CO2 and oil production. Notably, as the standard deviation was raised to 0.1, a small sweep region arose with the early CO2 breakthrough, which led to a worse flooding effect. Finally, this study exemplifies that a higher injection/production rate and CO2 alternating N2 injection strategies can improve oil recovery in highly heterogeneous reservoirs

A review of CO2 storage in view of safety and cost-effectiveness

The emissions of greenhouse gases, especially CO2, have been identified as the main contributor for global warming and climate change. Carbon capture and storage (CCS) is considered to be the most promising strategy to mitigate the anthropogenic CO2 emissions. This review aims to provide the latest developments of CO2 storage from the perspective of improving safety and economics. The mechanisms and strategies of CO2 storage, focusing on their characteristics and current status, are discussed firstly. In the second section, the strategies for assessing and ensuring the security of CO2 storage operations, including the risks assessment approach and monitoring technology associated with CO2 storage, are outlined. In addition, the engineering methods to accelerate CO2 dissolution and mineral carbonation for fixing the mobile CO2 are also compared within the second section. The third part focuses on the strategies for improving economics of CO2 storage operations, namely enhanced industrial production with CO2 storage to generate additional profit, and co-injection of CO2 with impurities to reduce the cost. Moreover, the role of multiple CCS technologies and their distribution on the mitigation of CO2 emissions in the future are summarized. This review demonstrates that CO2 storage in depleted oil and gas reservoirs could play an important role in reducing CO2 emission in the near future and CO2 storage in saline aquifers may make the biggest contribution due to its huge storage capacity. Comparing the various available strategies, CO2-enhanced oil recovery (CO2-EOR) operations are supposed to play the most important role for CO2 mitigation in the next few years, followed by CO2-enhanced gas recovery (CO2-EGR). The direct mineralization of flue gas by coal fly ash and the pH swing mineralization would be the most promising technology for the mineral sequestration of CO2. Furthermore, by accelerating the deployment of CCS projects on large scale, the government can also play its role in reducing the CO2 emissions

Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to harmonize with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introduces a spiking patch encoding layer to reinforce local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that effectively blends global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model capacity, and simulation steps. An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings accentuate the potential of our deep SNN architecture in seamlessly integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.Comment: 11 pages, 6 figure

Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling

Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple instance learning(MIL), where the predictions of unlabeled instances are supervised by classifying labeled bags. The MIL-based methods are relatively well studied with cogent performance achieved on classification but not on localization. Generally, they locate temporal regions by the video-level classification but overlook the temporal variations of feature semantics. To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics. Specifically, our model entails two components, the first is an unsupervised change-points detection module that detects change-points by learning the latent representations of video features in a temporal hierarchy based on their rates of change, and the second is an attention-based classification model that selects the change-points of the foreground as the boundaries. To evaluate the effectiveness of our model, we conduct extensive experiments on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3. The experiments show that our method outperforms current state-of-the-art methods, and even achieves comparable performance with fully-supervised methods.Comment: Accepted to ICCV 2023. arXiv admin note: text overlap with arXiv:2203.15187, arXiv:2003.12424, arXiv:2104.02967 by other author

Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China

Background: Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of ‘line lists’ with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus. Methods: We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by HealthMap, Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure. Results: Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou. Conclusions: We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries

Development of coupled THM models for reservoir stimulation and geo-energy production with supercritical CO2 as working fluid

Due to enhanced public awareness, the eco-friendly techniques in geo-energy exploitation, e.g. supercritical CO2 fracturing, have received extensive attention in the last two decades. In this dissertation, two specific numerical models have been developed to address the issues associated with utilization of supercritical CO2, like fracture creation, proppant placement and fracture closure in unconventional gas reservoirs, reservoir stimulation, heat production and CO2 sequestration in deep geothermal reservoirs, respectively. (a) In unconventional gas reservoir, the model consisting of classic fracture model, proppant transport model as well as temperature-sensitive fracturing fluids (CO2, thickened CO2 and guar gum) has been integrated into the popular THM coupled framework (TOUGH2MP-FLAC3D), which has the ability to simulate single fracture propagation driven by different fracturing fluids in non-isothermal condition. (b) To characterize the fracture network propagation and internal multi fluids behavior in deep geothermal reservoirs, an anisotropic permeability model on the foundation of the continuum anisotropic damage model has been developed and integrated into the popular THM coupled framework (TOUGH2MP-FLAC3D) as well. This model has the potential to simulate the reservoir stimulation and heat extraction based on a CO2-EGS concept. Attempting to improve the fracturing ability and proppant-carrying capacity of CO2, the unconventional gas reservoir model is applied in a fictitious model with the parameters of a typical tight gas reservoir. According to results, pure CO2 is inefficient to create a fracture in such tight gas reservoir. Its fracturing ability can be improved significantly by using CO2 thickener. In comparison with traditional guar gum, the leakage of thickened CO2 is higher. But considering the expansion of CO2 in the fracture, accounting for about 37% contribution in this case, the performance of thickened CO2 is comparable with traditional guar gum. Additionally, a linear correlation between the break down pressure and fracturing fluid viscosity has been observed. In non-isothermal conditions, the temporal and spatial leak-off rate and expansion effect distribute unevenly in fracture. Due to high temperature around fracture tip, High leak-off rate and expansion effect is found nearby the tip zone. During fracture initiation, the expansion effect plays a critical role. With continuous injection, the dominating factor in fracture-making is gradually switched to leak-off. Overall, fracturing is more sensitive to leak-off than fluid expansion. For proppant-carrying, thickened CO2 with light proppant can achieve a better proppant placement than heavy proppant, even better than the one transported by guar gum. Due to low specific heat capacity, thickened CO2 has a high gel breaking rate, resulting in better fracture support. In the application of the damage-permeability model at planned Dikili geothermal project, a fracture network with final SRV of 8.7×107 m³ and SRA of 2.2×106 m2 is created after injecting 90,000 kg CO2 in 250 hours. The maximum permeability of 1.3×10-13 m2 has been achieved in x- and z-directions. During heat production, a priority channel with high gas saturation is formed because of the viscosity difference between CO2 and water. The heat is mainly mined in this priority channel. Generally, for both water and CO2 injection, the driven pressure and average thermal capacity shows a positive correlation with injection rate, while inverse relation exists between eventual temperature and injection rate. Under the same mass injection rate, the driven pressure and average thermal capacity of water injection is higher than CO2 injection, whereas the ultimate produced temperature of water injection is lower. Therefore, CO2 as working fluid for heat extraction has the benefits of low driving pressure and is beneficial for realizing relatively stable heat mining. In addition, the injected CO2 is detained in geothermal reservoir due to leak off, which can be regarded as geologically sequestrated CO2. Furthermore, the sequestrated CO2 has a positive relationship with injection rate. After 30 years of geothermal production, up to 950,000 tons CO2 is sequestrated at an injection rate of 100kg/s, demonstrating the potential contribution of CO2-ESG on the sequestration of CO2

Building an acoustical-environment monitoring system

The purpose of this project is to design and develop an acoustical-environment monitoring system. The prototype of this system is able to monitor ambient noise level, determine the impulse response and reverberation time of a room, calculate the speech transmission index (STI), and act as a sound masking system. The method used to achieve these goals is to continually monitor the amplitude of incoming sound, while also radiating low-level maximum length sequence (MLS) sound signals. The MLS signal received by microphones is then cross-correlated with the output signal in order to determine the impulse response of the room. The STI is determined with knowledge of the ambient noise level, impulse response, and a user defined speech level. To justify continuously emitting pseudorandom noise, the system also provides a sound masking function by filtering the emitted MLS to match the characteristics of masking sound. In doing so, the device meets the demand for privacy in buildings that lack a Heating, Ventilation, and Air Conditioning (HVAC)system such as newly developed sustainable buildings. The prototype consists of a laptop, running MATLAB script, as well as the necessary peripherals such as microphones and loud speakers. A series of programs were written to monitor sound level, emit and filter MLS signals, measure the room response, calculate the STI,and determine the reverberation time for eight different octave bands. An A-weighting filter was also designed to adjust the measured ambient noise level in order to correspond to human hearing. Our group has begun to lay the foundation for the project; however, there are still improvements to be made. The prototype’s sound level measurement has been verified by a commercial sound level meter; however, due to time constraints, there has been a limited amount of testing for the impulse response, STI, and reverberation time measurements. It is recommended that future groups work to improve the timing of the impulse response function, perhaps by using a language or data-acquisition method that would allow for time-stamping the input and output signals, and run further testing. It is also recommended that future groups further test the quality of the sound masking system and rigorously determine the best MLS filter required for the task. It had also been found that MATLAB's built in function "xcorr" ran faster than the originally planned hadamard transform method [2] of cross-correlation. It is recommended that future groups look into this part of the code specifically. The hadamard transform method may have been necessary in the past, as [2] had been published in 1983 and assumes 1 microsecond per calculation, but there are possible alternatives including "xcorr" and "cconv" functions in MATLAB. It is suspected that further optimization of the code would result from examining this issue. Of course, any further optimization by future groups would be beneficial to the project as well.Science, Faculty ofPhysics and Astronomy, Department ofUnreviewedUndergraduat

Investigative coupled thermo-hydro-mechanical modelling approach for geothermal heat extraction through multistage hydraulic fracturing from hot geothermal sedimentary systems

The meaningful utilization of artificially created multiple fractures in tight formations is associated with the performance behavior of such flow channels, especially in the case of thermal energy extraction from sedimentary geothermal system. In this study, an innovative idea is presented to develop a numerical model for geothermal energy production based on concrete physical performance of an artificially created tensile multi-fracture system in a simplified manner. The state-of-the-art software FLAC3Dplus-TOUGH2MP-TMVOC are integrated to develop a coupled thermo-hydro-mechanical (THM) fictive model for constructing a multi-fracture scheme and estimating heat extraction performance. By incorporating the actual fracture width of newly created subsequent fracture under the effect of stress shadow, cubic law is implemented for fluid flow and geothermal energy production. The results depict that fracture spacing plays a vital role in the energy contribution through multiple fractures. Afterwards, a field case study to design huge multiple hydraulic fractures was performed in the geothermal well GB X1 in North Germany. The attenuation of fracture propagation becomes more significant when massive multiple fracturing operation is performed especially in the case of lower fracture spacing. The fictive model results will be extended to study the geothermal utilization of the North German basin through massive multiple fractures in our future work