The Zarga and Ghazal formations constitute important reservoirs across the Muglad Basin, Sudan. Nevertheless, the sedimentology and diagenesis of these reservoir intervals have hitherto received insignificant research attention. Detailed understanding of sedimentary facies and diagenesis could enhance geological and geophysical data for better exploration and production and minimize risks. In this study, subsurface reservoir cores representing the Zarga formation (1114.70-1118.50 m and 1118.50-1125.30 m), and the Ghazal formation (91,403.30-1406.83 m) were subjected to sedimentological (lithofacies and grain size), petrographic/mineralogic (thin section, XRD, SEM), and petrophysical (porosity and permeability) analyses to describe their reservoir quality, provenance, and depositional environments. Eight (8) different lithofacies, texturally characterized as moderately to well-sorted, and medium to coarse-grained, sub-feldspathic to feldspathic arenite were distinguished in the cored intervals. Mono-crystalline quartz (19.3-26.2%) predominated over polycrystalline quartz (2.6-13.8%), feldspar (6.6-10.3%), and mica (1.4-7.6%) being the most prominent constituent of the reservoir rocks. Provenance plot indicated the sediments were from a transitional continental provenance setting. The overall vertical sequence, composition, and internal sedimentary structures of the lithofacies suggest a fluvial-to-deltaic depositional environment for the Ghazal formation, while the Zarga formation indicated a dominant deltaic setting. Kaolinite occurs mainly as authigenic mineral, while carbonates quantitatively fluctuate with an insignificant amount of quartz overgrowths in most of the analyzed cores. Integration of XRD, SEM, and thin section analysis highlights that kaolinite, chlorite, illite, and smectite are present as authigenic minerals. Pore-destroying diagenetic processes (e.g. precipitation, cementation, and compaction etc.) generally prevailed over pore-enhancing processes (e.g. dissolution). Point-counted datasets indicate a better reservoir quality for the Ghazal formation (ɸ = 27.7% to 30.7%; K = 9.65 mD to 1196.71 mD) than the Zarga formation (17.9% to 24.5%; K = 1051.09 mD to 1090.45 mD).
The Abu Gabra and Bentiu formations are widely distributed within the interior Muglad Basin. Recently, much attention has been paid to study, evaluate and characterize the Abu Gabra Formation as a proven reservoir in Muglad Basin. However, few studies have been documented on the Bentiu Formation which is the main oil/gas reservoir within the basin. Therefore, 33 core samples of the Great Moga and Keyi oilfields (NE Muglad Basin) were selected to characterize the Bentiu Formation reservoir using sedimentological and petrophysical analyses. The aim of the study is to de-risk exploration activities and improve success rate. Compositional and textural analyses revealed two main facies groups: coarse to-medium grained sandstone (braided channel deposits) and fine grained sandstone (floodplain and crevasse splay channel deposits). The coarse to-medium grained sandstone has porosity and permeability values within the range of 19.6% to 32.0% and 1825.6 mD to 8358.0 mD respectively. On the other hand, the fine grained clay-rich facies displays poor reservoir quality as indicated by porosity and permeability ranging from 1.0 to 6.0% and 2.5 to 10.0 mD respectively. A number of varied processes were identified controlling the reservoir quality of the studies samples. Porosity and permeability were enhanced by the dissolution of feldspars and micas, while presence of detrital clays, kaolinite precipitation, iron oxides precipitation, siderite, quartz overgrowths and pyrite cement played negative role on the reservoir quality. Intensity of the observed quartz overgrowth increases with burial depth. At great depths, a variability in grain contact types are recorded suggesting conditions of moderate to-high compactions. Furthermore, scanning electron microscopy revealed presence of micropores which have the tendency of affecting the fluid flow properties in the Bentiu Formation sandstone. These evidences indicate that the Bentiu Formation petroleum reservoir quality is primarily inhibited by grain size, total clay content, compaction and cementation. Thus, special attention should be paid to these inhibiting factors to reduce risk in petroleum exploration within the area.
Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.