Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

<abstract><p>Many variations of the algebraic Riccati equation (ARE) have been used to study nonlinear system stability in the control domain in great detail. Taking the quaternion nonsymmetric ARE (QNARE) as a generalized version of ARE, the time-varying QNARE (TQNARE) is introduced. This brings us to the main objective of this work: finding the TQNARE solution. The zeroing neural network (ZNN) technique, which has demonstrated a high degree of effectiveness in handling time-varying problems, is used to do this. Specifically, the TQNARE can be solved using the high order ZNN (HZNN) design, which is a member of the family of ZNN models that correlate to hyperpower iterative techniques. As a result, a novel

Exposure to cryogenic liquids can significantly impact the petrophysical properties of rock, affecting its density, porosity, permeability, and elastic properties. These effects can have important implications for various applications, including oil and gas production and carbon sequestration. Cryogenic liquid fracturing is a promising alternative to traditional hydraulic fracturing for exploiting unconventional oil and gas resources and geothermal energy. This technology offers several advantages over traditional hydraulic fracturing, including reduced water consumption, reduced formation damage, and a reduced risk of flow-back fluid contamination. In this study, an updated review of recent studies demonstrates how the

Rock type identification is very important task in Reservoir characterization in order to constrict robust reservoir models. There are several approaches have been introduced to define the rock type in reservoirs and each approach should relate the geological and petrophysical properties, such that each rock type is proportional to a unique hydraulic flow unit. A hydraulic flow unit is a reservoir zone that is laterally and vertically has similar flow and bedding characteristics. According to effect of rock type in reservoir performance, many empirical and statistical approaches introduced.  In this paper Cluster Analysis technique is used to identify the rock groups in tertiary reservoir for Khabaz oil field by analyses variation o

Knowing the distribution of the mechanical rock properties and in-situ stresses for the field of interest is essential for many applications concerning reservoir geomechanics, including wellbore instability analysis, hydraulic fracturing, sand production, reservoir compaction, subsidence and water/gas injection throughout the filed life cycle. Determining the rock's mechanical properties is challenging because they cannot be directly measured at the borehole. The recovered carbonate core samples are limited and only provide discrete data for specific depths. This study focuses on creating a detailed 1D geomechanical model of the Mishrif reservoir in the Nasriyah oil field to identify the fault regime type for each unit in the format

Polyvinyl chloride (PVC) finds use in various industries due to its versatility but is not thermostable, serving as a barrier to causing degradation and release of hydrochloric acid (HCl) under heat stress conditions. Lead and organotin compounds have traditionally been used for stabilizer purpose to discourage such degradation but pose serious ecological as well as health risks. As a counter measure, bio-based stabilizers from plant oils have become popular as a green and sustainable alternative. This review looks back at recent developments in plant oil-based stabilizers for PVC, focusing on the mechanisms of stabilization, sustainability, and cost-effectiveness. We review plant oils such as tung oil, castor oil, and soybean oil,

The aim of this study is for testing the applicability of Ramamoorthy and Murphy method for identification of predominant pore fluid type, in Middle Eastern carbonate reservoir, by analyzing the dynamic elastic properties derived from the sonic log. and involving the results of Souder, for testing the same method in chalk reservoir in the North Sea region. Mishrif formation in Garraf oilfield in southern Iraq was handled in this study, utilizing a slightly-deviated well data, these data include open-hole full-set logs, where, the sonic log composed of shear and compression modes, and geologic description to check the results. The Geolog software is used to make the conventional interpretation of porosity, lithology, and saturation. Also,

Fluid-structure interaction method is performed to predict the dynamic characteristics of axial fan system. A fluid-structure interface physical environment method (monolithic method) is used to couple the fluid flow solver with the structural solver. The integration of the three-dimensional Navier-Stokes equations is performed in the time Doman, simultaneously to the integration of the three dimensional structural model. The aerodynamic loads are transfer from the flow to structure and the coupling step is repeated within each time step, until the flow solution and the structural solution have converged to yield a coupled solution of the aeroelastic set of equations. Finite element method is applied to solve numerically

The water injection of the most important technologies to increase oil production from petroleum reservoirs. In this research, we developed a model for oil tank using the software RUBIS for reservoir simulation. This model was used to make comparison in the production of oil and the reservoir pressure for two case studies where the water was not injected in the first case study but adding new vertical wells while, later, it was injected in the second case study. It represents the results of this work that if the water is not injected, the reservoir model that has been upgraded can produce only 2.9% of the original oil in the tank. This case study also represents a drop in reservoir pressure, which was not enough to support oil production

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings