Unpredicted, rapid plume elongation has been observed at subsurface CO2 storage projects worldwide, exemplified by the Sleipner project. We show that conventionally ignored centimeter‐meter scale ...heterogeneity in capillary pressure characteristics can manifest as rapid field‐scale, decameter‐kilometer, plume migration. We analyze the effect in the Goldeneye field, UK, a proposed storage site with a unique combination of sample/data accessibility and generality as an archetype sandstone reservoir. We overcome previous barriers by characterizing in greater detail over larger scales—the 65 m reservoir height at cm‐m resolution—and through use of an upscaling scheme which resolves small‐scale heterogeneity impacts in field‐scale simulations. These models reveal that significant early time retardation of buoyantly rising CO2 plumes is followed by rapid migration under the caprock in the presence of anisotropic, layered heterogeneities. Lateral migration speeds can be enhanced by 200%, placing first‐order controls on fluid flow and providing a mechanistic explanation for field observations.
Plain Language Summary
Geological carbon storage is a promising technique to reduce greenhouse gas emissions. Captured carbon dioxide is generally injected into a subsurface reservoir over 1,000 m underground, displacing resident brine and eventually becoming trapped underneath a low‐permeability caprock seal. However, at several industrial‐scale storage sites around the world, the carbon dioxide has migrated laterally away from the injection well much quicker than anticipated and followed pathways that are not predicted by models. It is crucial that these models can predict the migration and demonstrate safe storage to owners and policy makers. In this work, we show that one source of the discrepancy is the omission of the impacts of small‐scale rock heterogeneities in these models. We experimentally characterize rock cores from a North Sea reservoir at high resolution, and through rigorous multiscale modeling show that centimeter‐meter‐scale heterogeneities in the rock structure, for example, small mudstone layers in sandstone, can cause rapid migration at larger, meter‐kilometer scales. Carbon dioxide can migrate up to 200% faster in the presence of layered heterogeneities. These heterogeneities are ubiquitous in nature and provide an explanation for the behavior seen at storage sites worldwide. Our modeling approach incorporates this behavior, improving the predictability and control of storage operations.
Key Points
Core‐to‐field multiscale experiments, modeling, and upscaling elucidate the impacts of small‐scale heterogeneity on subsurface CO2 storage
Dm‐m‐scale layered capillary heterogeneities can cause rapid field‐scale plume migration, 100–200% faster than homogeneous and isotropic cases
Capillary heterogeneity places leading order controls on plume migration and could contribute to unexpected CO2 migration seen worldwide
In this work, we analyze the characterization of drainage multiphase flow properties on heterogeneous rock cores using a rich experimental data set and mm‐m scale numerical simulations. Along with ...routine multiphase flow properties, 3‐D submeter scale capillary pressure heterogeneity is characterized by combining experimental observations and numerical calibration, resulting in a 3‐D numerical model of the rock core. The uniqueness and predictive capability of the numerical models are evaluated by accurately predicting the experimentally measured relative permeability of N2—DI water and CO2—brine systems in two distinct sandstone rock cores across multiple fractional flow regimes and total flow rates. The numerical models are used to derive equivalent relative permeabilities, which are upscaled functions incorporating the effects of submeter scale capillary pressure. The functions are obtained across capillary numbers which span four orders of magnitude, representative of the range of flow regimes that occur in subsurface CO2 injection. Removal of experimental boundary artifacts allows the derivation of equivalent functions which are characteristic of the continuous subsurface. We also demonstrate how heterogeneities can be reorientated and restructured to efficiently estimate flow properties in rock orientations differing from the original core sample. This analysis shows how combined experimental and numerical characterization of rock samples can be used to derive equivalent flow properties from heterogeneous rocks.
Key Points
Drainage multiphase flow properties are characterized on heterogeneous rocks using core flood experiments and 3‐D numerical simulations
3‐D numerical models are validated by predicting experimental gas‐water relative permeabilities at multiple fractional flows and two flow rates
Equivalent relative permeabilities incorporating mm‐m scale heterogeneities are derived across a range of Nc, relevant for subsurface flow
Microglial activation in multiple sclerosis has been postulated to contribute to long-term neurodegeneration during disease. Fingolimod has been shown to impact on the relapsing remitting phase of ...disease by modulating autoreactive T-cell egress from lymph organs. In addition, it is brain penetrant and has been shown to exert multiple effects on nervous system cells.
In this study, the impact of fingolimod and other sphingosine-1-phosphate receptor active molecules following lysophosphotidyl choline-induced demyelination was examined in the rat telencephalon reaggregate, spheroid cell culture system. The lack of immune system components allowed elucidation of the direct effects of fingolimod on CNS cell types in an organotypic situation.
Following demyelination, fingolimod significantly augmented expression of myelin basic protein in the remyelination phase. This increase was not associated with changes in neurofilament levels, indicating de novo myelin protein expression not associated with axonal branching. Myelin wrapping was confirmed morphologically using confocal and electron microscopy. Increased remyelination was associated with down-regulation of microglial ferritin, tumor necrosis factor alpha and interleukin 1 during demyelination when fingolimod was present. In addition, nitric oxide metabolites and apoptotic effectors caspase 3 and caspase 7 were reduced during demyelination in the presence of fingolimod. The sphingosine-1-phosphate receptor 1 and 5 agonist BAF312 also increased myelin basic protein levels, whereas the sphingosine-1-phosphate receptor 1 agonist AUY954 failed to replicate this effect on remyelination.
The results presented indicate that modulation of S1P receptors can ameliorate pathological effectors associated with microglial activation leading to a subsequent increase in protein and morphological markers of remyelination. In addition, sphingosine-1-phosphate receptor 5 is implicated in promoting remyelination in vitro. This knowledge may be of benefit for treatment of chronic microglial inflammation in multiple sclerosis.
Carbonate rock reservoirs are dominated by heterogeneity across a large and continuous range of spatial scales. We study the impact of heterogeneities on relative permeability and residual trapping ...for three carbonate rocks selected for their distinct spatial scales of rock texture. The Indiana limestone comprises millimeter‐scale heterogeneities, the Estaillades limestone consists of half‐centimeter‐scale heterogeneities, and the Edwards dolomite includes decimeter‐scale heterogeneity. Along with routine characterization of rock samples, steady‐state N2–deionized water drainage relative permeability measurements are made for each rock at two distinct total flow rates, at least 1 order of magnitude apart. The variation in flow potential across the core results in observations of fluid distribution, core‐average relative permeability, and residual trapping obtained for a range of continuum‐scale capillary number 0.02<Nc=ΔPLHΔPc<4.2. The relative permeability curves for all rocks shift to the right of the water saturation axis with increasing flow potential; the nitrogen relative permeability increases while the water relative permeability decreases. However, the magnitude of the shift depends on the spatial scale of heterogeneity. An inspection of 3D saturation distributions in the cores and estimation of the capillary numbers of flow shows that the rock with the largest heterogeneity is capillary flow dominated throughout the range of injection rates tested; observations in the Indiana and Estaillades carbonates traverse capillary to viscous dominated flow regimes, with commensurate flow rate dependence in the relative permeability. In all cases, residual trapping is poorly described by the Land model.
Key Points
Steady‐state multiphase flow is observed with 3D X‐ray CT in three carbonate rocks with mm–dm‐scale heterogeneities
Significant heterogeneity can result in decreased flow rate dependency in kr due to the dominance of capillarity over varying viscous flow
Residual trapping is poorly described by Land model in highly heterogeneous carbonate rocks
Football is the most popular sport in the world with four billion fans all over the world. Reportedly, the violence incidence rates are high during or after the matches. The violent or destructive ...behavior carried out by a person or player, who watches or plays the game in the stadium is known as football hooliganism. To prevent or control the violence, a real time violence detection system is exclusively needed to monitor the behavior of the crowd and players to take necessary action before the violence is about to happen. Even it is necessary for the system to find whether the attack is non-intentional or intentional in the game. In this paper, a real time violence detection system is proposed which processes the huge input streaming data and recognize the violence with human intelligence simulation. The input to the system is the enormous amount of real time video streams from different sources which is processed in Spark framework. In the Spark framework, the frames are separated and the features of individual frames are extracted by using HOG (Histogram of Oriented Gradients) function. Then the frames are labeled based on features as violence model, human part model and negative model, which are used to train the Bidirectional Long Short-Term Memory (BDLSTM) network for recognition of violence scenes. The bidirectional LSTM can access the information both in forward and reverse direction. Thus the output is generated in context to both past and future information. The network is trained with the violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. Moreover to make the model robust to violence detection, we have created a dataset with 410 video clips having non-violence scenes and 409 video clips having violence scenes, acquired from the football stadium. The performance of this model is validated and it proves the sturdiness of the system with an accuracy of 94.5 percentage in recognizing the violent action.
Hydrogen storage in subsurface aquifers or depleted gas reservoirs represents a viable long-term energy storage solution. There is currently a scarcity of subsurface petrophysical data for the ...hydrogen system. In this work, we determine the wettability and Interfacial Tension (IFT) of the hydrogen-brine-quartz system using captive bubble, pendant drop and in-situ 3D micro-Computed Tomography (CT) methods. Effective contact angles ranged between 29° and 39° for pressures 6.89–20.68 MPa and salinities from distilled water to 5000 ppm NaCl brine. In-situ methods, novel to hydrogen investigations, confirmed the water-wet system with the mean of the macroscopic and apparent contact angle distributions being 39.77° and 59.75° respectively. IFT decreased with increasing pressure in distilled water from 72.45 mN/m at 6.89 MPa to 69.43 mN/m at 20.68 MPa. No correlation was found between IFT and salinity for the 1000 ppm and 5000 ppm brines. Novel insights into hydrogen wetting in multiphase environments allow accurate predictions of relative permeability and capillary pressure curves for large scale simulations.
•Comprehensive characterisation of hydrogen-brine-quartz wettability.•Novel investigation of in-situ contact angle for underground hydrogen storage.•Contact angle on smooth rock surface 27–39° and at in situ conditions 39.77–59.75°.•Deficit curvature analysis confirms a water-wet system.•IFT from 72.45 mN/m at 6.89 MPa to 69.43 mN/m at 20.68 MPa.
•A deep learning method is proposed using different optimizers and transfer learning to classify Pneumonia patients.•Preparing a dataset of around 5300 X-ray images for pneumonia detection.•The ...proposed deep transfer learning method is trained on a benchmark open dataset of chest x-ray images.•Presenting the optimization results, precision, recall, accuracy, and F1-score for proposed method.•In proposed method achieve better accuracy in the detection rate than other techniques.
Pneumonia is a disease that leads to the death of individuals within a short period since the flow of fluid in the lungs. Hence, initial diagnosis and drugs are very important to avoid the progress of the disease. This paper proposes a novel deep learning approach for automatic detection of pneumonia using deep transfer learning to simplify the detection process with improved accuracy. This work was aimed to preprocess the input chest X-ray images to identify the presence of pneumonia using U-Net architecture based segmentation and classifies the pneumonia as normal and abnormal (Bacteria, viral) using pre-trained on ImageNet dataset models such as ResNet50, InceptionV3, InceptionResNetV2. Besides, to extract the efficient features and improve accuracy of pre-trained models two optimizers, namely, Adam and Stochastic Gradient Descent (SGD) used and its performances are analyzed with batch sizes of 16 and 32. Based on the values obtained, the performances of undertaken pre-trained models are analyzed and compared with other Convolutional Neural Network (CNN) models such as DenseNet-169+SVM, VGG16, RetinaNet + Mask RCNN, VGG16 and Xception, Fully connected RCNN, etc using various measures. From the results observed that the proposed ResNet50 model work achieved 93.06% accuracy, 88.97 % precision rate, 96.78% Recall rate and 92.71% F1-score rate, which than is higher than the other models aforementioned.
Small‐scale heterogeneities in multiphase flow properties fundamentally control the flow of fluids from very small to very large scales in geologic systems. Inability to characterize these ...heterogeneities often limits numerical model descriptions and predictions of multiphase flow across scales. In this study, we evaluate the ability of pore network models (PNMs) to characterize multiphase flow heterogeneity at the millimeter scale using X‐ray micro‐computed tomography images of centimeter‐scale rock cores. Specifically, PNM capillary pressure and relative permeability output are used to populate a Darcy‐scale numerical model of the rock cores. These pore‐network‐derived Darcy‐scale simulations lead to accurate predictions of core‐average relative permeability, and water saturation, as validated by independent experimental data sets from the same cores and robust uncertainty analysis. Results highlight that heterogeneity in capillary pressure characteristics is more important for predicting local and upscaled flow behavior than heterogeneity in permeability or relative permeability. The leading uncertainty in core‐average relative permeability is driven not by the image processing or PNM extraction but rather by ambiguity in capillary pressure boundary condition definition in the Darcy‐scale simulator. This workflow enables characterization of local capillary heterogeneity and core‐averaged multiphase flow properties while circumventing the need for the most complex experimental observations conventionally required to obtain these properties.
Plain Language Summary
To understand how fluids flow in subsurface rocks, it is often necessary to perform laborious and expensive experiments aimed at replicating the subsurface pressure and temperature conditions. In this study, we propose and test a new modeling‐based approach using high‐resolution images capable of describing the structure and pore space of the rock at a resolution 10 times smaller than the width of a typical human hair. We show that with these high‐resolution images, along with a few routine rock property measurements, it is possible to predict the distribution of fluids in the rocks at range of subsurface fluid flow conditions. This digital, or experiment‐free, approach has the potential to redefine how we parameterize larger‐scale models of problems such as contaminant flow in aquifers or carbon dioxide migration and trapping in carbon capture and storage reservoirs.
Key Points
Pore network models extracted from X‐ray micro‐computed tomography scans can predict capillary heterogeneity in subdomains of core samples
Darcy‐scale simulation results, parameterized with pore network model output, agree well with independent experimental measurements
A digital rocks approach is presented for multiphase characterization that requires no experimental calibration
The characterization of multiphase flow properties is essential for predicting large‐scale fluid behavior in the subsurface. Insufficient representation of small‐scale heterogeneities has been ...identified as a major gap in conventional reservoir simulation workflows. We systematically evaluated the workflow developed by Jackson et al. (2018), https://doi.org/10.1029/2017wr022282 for use on rocks with complex porosity and capillary heterogeneities. The workflow characterizes capillary heterogeneity at the millimeter scale. The method is a numerical history match of a coreflood experiment with the 3D saturation distribution as a matching target and the capillary pressure characteristics as a fitting parameter. Coreflood experimental datasets of five rock cores with distinct heterogeneities were analyzed: two sandstones and three carbonates. The sandstones exhibit laminar heterogeneities. The carbonates have isotropic heterogeneities at a range of length scales. We found that the success of the workflow is primarily governed by the extent to which heterogeneous structures are resolved in the X‐ray imagery. The performance of the characterization workflow systematically improved with increasing characteristic length scales of heterogeneities. Using the validated models, we investigated the flow rate dependency of the upscaled relative permeability. The findings showed that the isotropic heterogeneity in the carbonate sample resulted in non‐monotonic behavior; initially the relative permeability increased, and then subsequently decreased with increasing flow rate. The work underscores the importance of capturing small‐scale heterogeneities in characterizing subsurface fluid flows, as well as the challenges in doing so.
Key Points
Integrating numerical modeling and experimental observations is essential to characterizing small‐scale heterogeneity for subsurface flow
Capillary heterogeneity characterization in carbonates was only successful when key features were resolved in X‐ray imagery
Isotropic capillary heterogeneity in carbonates results in a non‐monotonic rate‐dependant relative permeability
Natural disasters could be defined as a blend of natural risks and vulnerabilities. Each year, natural as well as human-instigated disasters, bring about infrastructural damages, distresses, revenue ...losses, injuries in addition to huge death roll. Researchers around the globe are trying to find a unique solution to gather, store and analyse Big Data (BD) in order to predict results related to flood based prediction system. This paper has proposed the ideas and methods for the detection of flood disaster based on IoT, BD, and convolutional deep neural network (CDNN) to overcome such difficulties. First, the input data is taken from the flood BD. Next, the repeated data are reduced by using HDFS map-reduce (). After removal of repeated data, the data are pre-processed using missing value imputation and normalization function. Then, centred on the pre-processed data, the rule is generated by using a combination of attributes method. At the last stage, the generated rules are provided as the input to the CDNN classifier which classifies them as a) chances for the occurrence of flood and b) no chances for the occurrence of a flood. The outcomes obtained from the proposed CDNN method is compared parameters like Sensitivity, Specificity, Accuracy, Precision, Recall and F-score. Moreover, when the outcomes is compared other existing algorithms like Artificial Neural Network (ANN) & Deep Learning Neural Network (DNN), the proposed system gives is very accurate result than other methods.
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