Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a critical parameter that defines the porosity-permeability relationships of each hydraulic flow unit. Additionally, the flow zone indicator method proves valuable in estimating permeability accurately. In this study, we demonstrate the application of the flow zone indicator method to determine hydraulic flow units within the Khasib formation. By analyzing core data and calculating the Rock Quality Index (RQI) and Flow Zone Indicator (∅Z), we differentiate the formation into four hydraulic flow units based on FZI values. Specifically, HFU 1 represents a rock of poor quality, corresponding to compact and chalky limestone. HFU 2 represents intermediate quality, corresponding to argillaceous limestone, while HFU 3 represents good quality, corresponding to porous limestone. Lastly, HFU 4 signifies an excellent reservoir rock quality characterized by vuggy limestone. By establishing a permeability equation that correlates with effective porosity for each rock type, we successfully estimate permeability. Comparing these estimated permeability values with core permeability reveals a strong agreement with a high correlation coefficient of 0.96%. Consequently, the flow zone indicator method effectively classifies the Khasib formation into four distinct hydraulic flow units and provides an accurate and reliable means of determining permeability in the reservoir. The resulting permeability equations can be applied to wells and depth intervals lacking core measurements, further emphasizing the practical utility of the FZI method.
In the field of construction project management, time and cost are the most important factors to be considered in planning every project, and their relationship is complex. The total cost for each project is the sum of the direct and indirect cost. Direct cost commonly represents labor, materials, equipment, etc.
Indirect cost generally represents overhead cost such as supervision, administration, consultants, and interests. Direct cost grows at an increasing rate as the project time is reduced from its original planned time. However, indirect cost continues for the life of the project and any reduction in project time means a reduction in indirect cost. Therefore, there is a trade-off between the time and cost for completing construc
Wireless Sensor Networks (WSNs) are promoting the spread of the Internet for devices in all areas of
life, which makes it is a promising technology in the future. In the coming days, as attack technologies become
more improved, security will have an important role in WSN. Currently, quantum computers pose a significant
risk to current encryption technologies that work in tandem with intrusion detection systems because it is
difficult to implement quantum properties on sensors due to the resource limitations. In this paper, quantum
computing is used to develop a future-proof, robust, lightweight and resource-conscious approach to sensor
networks. Great emphasis is placed on the concepts of using the BB8
Surveillance cameras are video cameras used for the purpose of observing an area. They are often connected to a recording device or IP network, and may be watched by a security guard or law enforcement officer. In case of location have less percentage of movement (like home courtyard during night); then we need to check whole recorded video to show where and when that motion occur which are wasting in time. So this paper aims at processing the real time video captured by a Webcam to detect motion in the Scene using MATLAB 2012a, with keeping in mind that camera still recorded which means real time detection. The results show accuracy and efficiency in detecting motion