Early diagnosis of Coronavirus disease 2019 (COVID-19) is significantly important, especially in the absence or inadequate provision of a specific vaccine, to stop the surge of this lethal infection by advising quarantine. This diagnosis is challenging as most of the patients having COVID-19 infection stay asymptomatic while others showing symptoms are hard to distinguish from patients having different respiratory infections such as severe flu and Pneumonia. Due to cost and time-consuming wet-lab diagnostic tests for COVID-19, there is an utmost requirement for some alternate, non-invasive, rapid, and discounted automatic screening system. A chest CT scan can effectively be used as an alternative modality to detect and diagnose the COVID-19 infection. In this study, we present an automatic COVID-19 diagnostic and severity prediction system called COVIDC (COVID-19 detection using CT scans) that uses deep feature maps from the chest CT scans for this purpose. Our newly proposed system not only detects COVID-19 but also predicts its severity by using a two-phase classification approach (COVID vs non-COVID, and COVID-19 severity) with deep feature maps and different shallow supervised classification algorithms such as SVMs and random forest to handle data scarcity. We performed a stringent COVIDC performance evaluation not only through 10-fold cross-validation and an external validation dataset but also in a real setting under the supervision of an experienced radiologist. In all the evaluation settings, COVIDC outperformed all the existing state-of-the-art methods designed to detect COVID-19 with an F1 score of 0.94 on the validation dataset and justified its use to diagnose COVID-19 effectively in the real setting by classifying correctly 9 out of 10 COVID-19 CT scans. We made COVIDC openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidc.
The tight gas reserves in the Hangjinqi area are estimated at 700 × 109 m3. Since the exploration of the Hangjinqi, numerous wells are already drilled. However, the Hangjinqi remains an exploration area and has yet to become a gas field. Identifying a paleo-depositional framework such as braided channels is beneficial for exploration and production companies. Further, braided channels pose drilling risks and must be properly identified prior to drilling. Henceforth, based on the significance of paleochannels, this study is focused on addressing the depositional framework and sedimentary facies of the first member (P2x1) of the lower Shihezi formation (LSF) for reservoir quality prediction. Geological modeling, seismic attributes, and petrophysical modeling using cores, logs, interval velocities, and 3D seismic data are employed. Geological modeling is conducted through structural maps, thickness map, and sand-ratio map, which show that the northeastern region is uplifted compared to northwestern and southern regions. The sand-ratio map showed that sand is accumulated in most of the regions within member-1. Interval velocities are incorporated to calibrate the acoustic impedance differences of mudstone and sandstone lithologies, suggesting that amplitude reflection is reliable and amplitude-dependent seismic attributes can be employed. The Root Mean Square (RMS) attribute confirmed the presence of thick-bedded braided channels. The results of cores and logging also confirmed the presence of braided channels and channel-bars. The test results of wells J34 and J72 shows that the reservoir quality within member-1 of LSF is favorable for gas production within the Hangjinqi area.
Missakeswal is an important hydrocarbon field, lying on active foreland fold and thrust belt of Himalayan orogeny in Potwar plateau. Integrated study of 2D seismic data (SEG-Y, Navigation and seismic velocities) and well logs helps us to delineate the potential reservoir rock of the area. Seismic interpretation based on stratigraphic studies and well tops, aids to mark four reflectors; Chorgali, Sakesar, Lockhart and Basement. Time sections are converted to depth section using velocity analysis system to delineate subsurface structure. Besides this, fault-bounded anticlines and crustal shortening analysis of the depth sections, revealed that folding in the sedimentary successions pre-date reverse faulting and regime of the Potwar basin, is suitable for hydrocarbon accumulation. 2D modeling of the interpreted seismic sections confirms reverse faulting in the sedimentary successions and normal faulting in the basement. Moreover, Seismic Attributes Analysis has carried out which helps in understanding the lateral continuity, bedding sequences and thickness of desired beds highlighted the petroleum system and affirmed the interpretation. The identified structural variations would help in the understanding of the regional tectonic settings, besides this, reservoir character in terms of lateral thickness variation, fault offsets and lithological dissimilarities are achieved. It also reveals that carbonate successions of the Sakesar and Chorgali formations acted as potential reservoirs in Missakeswal area.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.