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The world first came to know the existence of COVID-19 (SARS-CoV-2) in December 2019. Initially, doctors struggled to diagnose the increasing number of patients due to less availability of testing kits. To help doctors primarily diagnose the virus, researchers around the world have come up with some radiology imaging techniques using the Convolutional Neural Network (CNN). Previously some research methods were based on X-ray images and others on CT scan images. Few research methods addressed both image types, with the proposed models limited to detecting only COVID and NORMAL cases. This limitation motivated us to propose a 42-layer CNN model that works for complex scenarios (COVID, NORMAL, and PNEUMONIA_VIRAL) and more complex scenarios (COVID, NORMAL, PNEUMONIA_VIRAL, and PNEUMONIA_ BACTERIA). Furthermore, our proposed model indicates better performance than any other previously proposed models in the detection of COVID-19. [ABSTRACT FROM AUTHOR]

In CO2 sequestration projects, assessing caprock structural stability is crucial to assure the success and reliability of the CO2 injection. However, since caprock experimental data are sparse, we applied a Monte Carlo (MC) algorithm to generate stochastic data from the given mean and standard deviation values. The generated data sets were introduced to a neural network (NN), including four hidden layers for classification purposes. The model was then used to evaluate organic-rich Draupne caprock shale failure in the Alpha structure, northern North Sea. The train and test were carried out with 75% and 25% of the input data, respectively. Following that, validation is accomplished with unseen data, yielding promising classification scores. The results show that introducing larger input data sizes to the established NN provides better convergence conditions and higher classification scores. Although the NN can predicts the failure states with a classification score of 97%, the structural reliability was significantly low compare to the failure results estimated using other method. Moreover, this indicated that during evaluating the field-scale caprock failure, more experimental data is needed for a reliable result. However, this study depicts the advantage of machine learning algorithms in geological CO2 storage projects compared with similar finite elements methods in the aspect of short fitting time, high accuracy, and flexibility in processing different input data sizes with different scales. [ABSTRACT FROM AUTHOR]

Characterization of caprock shale is critical in CO2 storage site evaluation because the caprock shale acts as a barrier for the injected buoyant CO2 plume. The properties of shales are complex and influenced by various processes; hence, it is challenging to evaluate the caprock quality. An integrated approach is therefore necessary for assessing seal integrity. In this study, we investigated the caprock properties of the Lower Jurassic Drake Formation shales from the proposed CO2 storage site Aurora (the Longship/Northern Lights CCS project), located in the Horda Platform area, offshore Norway. Wireline logs from 50 exploration wells, various 2D seismic lines, and two 3D seismic cubes were used to investigate the variations of the caprock properties. The Drake Formation was subdivided into upper and lower Drake units based on the lithological variations observed. Exhumation and thermal gradient influencing the caprock properties were also analyzed. Moreover, rock physics diagnostics were carried out, and caprock property maps were generated using the average log values to characterize the Drake Formation shales. In addiiton, pre-stack seismic-inverted properties and post-stack seismic attributes were assessed and compared to the wireline log-based analysis. The sediment source controlled at 61° N significantly influenced the depositional environment of the studied area, which later influenced the diagenetic processes and had various caprock properties. The upper and lower Drake units represent similar geomechanical properties in the Aurora area, irrespective of significant lithological variations. The Drake Formation caprock shale near the injection site shows less-ductile to less-brittle brittleness values. Based on the caprock thickness and shaliness in the Aurora injection site, Drake Formation shale might act as an effective top seal. However, the effect of injection-induced pressure changes on caprock integrity needs to be evaluated. [ABSTRACT FROM AUTHOR]

Injection-induced rock mechanical failure risks are critical in CO2 sequestration, and thus there is a need to evaluate these occurrences to ensure safe and reliable subsurface storage. A stress–strain-based numerical simulation can reveal the potential mechanical risks of any CO2 sites. This study investigated the hydromechanical effect on geomechanical failure due to injection-induced stress and pore pressure changes in the prospective CO2 storage site Smeaheia, offshore Norway. An inverted-seismic-property-driven 3D field-scale geomechanical model was carried out in the Smeaheia area to evaluate the rock failure and deformation risks in various pressure-build-up scenarios. A one-way coupling between the before- and after-injection pressure scenarios of nine different models has been iterated using the finite element method. The effect of the sensitivity of total pore volume and pore compressibility on rock mechanical deformation is also evaluated. Although various models illustrated comparative variability on failure potential, no model predicted caprock failure or fracture based on the Mohr–Coulomb failure envelope. Moreover, the lateral mechanical failure variation among different locations indicated the possibility to identify a safer injection point with less chances of leakage. In addition, the pore volume and pore compressibility significantly influence the mechanical behavior of the reservoir and caprock rocks. Although this analysis could predict better injection locations based on geomechanical behavior, a fluid simulation model needs to be simulated for assessing lateral and vertical plume migration before making an injection decision. [ABSTRACT FROM AUTHOR]

Evaluating any subsurface CO 2 storage site comprises the reservoir, seal, and overburden investigation to mitigate injection and storage-related complications. The Upper-Middle Jurassic Sognefjord, Fensfjord, and Krossfjord formation sandstones are potential CO 2 storage reservoirs at the Smeaheia area, northern North Sea. The Smeaheia area is located east of the Troll oil and gas field. The Upper Jurassic organic-rich Heather and Draupne Formation shales are the main seals for the sandstone reservoirs. In this study, we carried out a prestack seismic inversion to obtain elastic property cubes of acoustic impedance (AI), velocity ratio (Vp/Vs), and bulk density (RhoB). From these elastic cubes, we obtained the reservoir properties such as porosity (Phi), shaliness (V sh), and permeability (k) of Sognefjord, Fensfjord, and Krossfjord formations. We introduced two new equations to extract the shale volume and porosity cubes from the inverted elastic cubes in the present study. These equations are nonlinear based on the AI versus Vp/Vs rock physics template. Both equations are correlated first with the well log data and then applied on the elastic property cubes (AI versus Vp/Vs) to obtain the V sh and Phi property cubes. An additional porosity cube (PhiD) was generated from the inverted RhoB for comparison. Finally, using an empirical equation, permeability was extracted from the porosity cube. The reservoir properties we derived from 3D seismic, in addition to the well log, revealed the vertical and lateral variations of porosity, shaliness, and permeability in the Smeaheia area. We used these reservoir properties to infer the depositional environment and the viability of reservoirs for CO 2 storage. The depositional environments identified were shoreface and deltaic from the extracted sand-body geometries. We found the Sognefjord Formation possessing the best reservoir properties, followed by the Fensfjord Formation as the secondary storage potential. In contrast, the Krossfjord Formation owed the lowest reservoir quality in the Smeaheia area. • Our new equations helped to reveal the lateral variations in reservoirs and their depositional environment. • The Sognefjord is the best, and Fensfjord Formation is the second-best quality reservoir in the area for CO 2 storage. • The Krossfjord Formation is the least suitable for CO 2 storage in the Smeaheia area. [ABSTRACT FROM AUTHOR]