Unconventional resources include oil and gas present in shale, tight sandstone and tight limestone formations. Shale oil can be produced from deposits of shale with estimates of about 5 trillion barrels of oil in place around the world. Because of the high content of clay in the shale formations, water-based drilling fluids tend to cause wellbore instability problems when drilling this type of formations. When it comes in contact with water, clay starts to react, swell and/or disperse leading to shale disintegration and sloughing. As a result of shale sloughing down into the borehole, cleaning efficiency of drilling fluids decreases significantly. Moreover, tight hole problem is expected which may cause drillpipe to get stuck and, as a result, increases non-productive time and well construction cost.Several types of shale inhibitive drilling fluids were developed using different shale inhibitors and stabilizers. Developing an inhibitive drilling fluid with long-term inhibition can eliminate the need for unnecessary casing and reduce tripping time. Multiple formations including the shale formation can be drilled and cased in one hole section. This paper summarizes preliminary laboratory testing results for characterizing one shale sample and assessing the interactions with different water-based mud systems. Shale characterization included determination of mineralogical composition using X-ray diffraction and determination of cation exchange capacity (CEC) while shale-mud interactions evaluation included swelling, dispersion and inhibition durability tests.

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Thermal operations are the most effective enhanced oil recovery (EOR) techniques used to increase hydrocarbon production, especially in heavy oil reservoirs. Thermal EOR involves injecting of steam or hot fluids into underground reservoirs to alter the physical properties, specifically, the fluid viscosity and effective mobility. Numbers of mathematical (numerical or analytical) models were developed to estimate the temperature variations during such operations, however, those models assume constant fluid velocity throughout the reservoir, then, severe estimations errors could be generated. The objective of this paper is to develop a new approach for determining the temperature distribution during thermal-EOR processes, and the heat propagations with time and distance from the wellbore with acceptable tolerance. The main aim of this work is to develop a reliable model to predict the temperature distributions in porous rocks during thermal EOR operations. Artificial intelligence (AI) methods were utilized to compute the temperature profiles, those models would minimize the complexity and uncertainties of numerical approaches. The impact of formation permeability, injection time and distance from wellbore were considered to develop effective models. To ensure a high level of model reliability, more than 220 data set was used for training and testing the proposed models. Temperature distribution was determined using the neural network, fuzzy logic system, and generalized intelligent networks. Different model's parameters were used to optimize the intelligent networks, average absolute error and correlation coefficient were utilized to measure the model performance. ANN model showed the best prediction performance, an average absolute error of 6.2 % and a correlation coefficient of 0.98 was obtained using unseen data set.

Background: COVID-19 pandemic becomes a great threat due to continuous rise of the global incidence and emergence of multiple waves of infections in many countries of the world. The diversity of this infection varies from country to country and knowledge regarding demographic characteristics of this infection is essential to combat the pandemic situation. Objective: The aim of this study was to characterize the demographic profile of rRT-PCR (real time reverse transcriptase-polymerase chain reaction) confirmed COVID-19 cases and to provide a timeline regarding rates of infection. Materials and methods: This cross- sectional study was conducted at the Department of Virology of Sir Salimullah Medical College, Dhaka, Bangladesh from May, 2020 to April, 2021. rRT-PCR test was performed to detect SARS-COV2 in 35001 clinical samples and their demographic characteristics were analyzed. Results: Out of 35001 suspected cases,5008 (14.3%) were tested positive of which 58.0% belongs to the age group between 18–45 years.The majority of the cases were male (69.0%). Most of the positive cases became negative (85.0%) within three weeks of infection by rRTPCR test. The highest percentage (29.2%) of confirmed COVID-19 cases was reported in the month of May 2020, then gradual decrease in the subsequent months followed by a sharp rise to 27.2% in the month of March, 2021. Conclusion: This study shows an overall 14.3% positivity among suspected COVID-19 cases where adult males between 18-45 years are more commonly suffered. However, most of the infected persons become rRT-PCR negative within three weeks’ time. A second surge of SARS-COV2 infection has been documented in this study that coincided with second wave of COVID-19 in Bangladesh. Sir Salimullah Med Coll J 2022; 30: 9-13