The map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in permeability prediction and compared its results with the flow zone indicator methods for a carbonate heterogeneous Iraqi formation. The methodology of the research can be Summarized by permeability was estimated by using two methods: Flow zone indicator and Artificial intelligence, two reservoir models are built, where the difference between them is in permeability method estimation, and the simulation run will be conducted on both of the models, and the permeability estimation methods will be examined by comparing their effect on the model history matching. The results showed that the model with permeability predicted by using artificial intelligence matched the observed data for different reservoir responses more accurately than the model with permeability predicted by the flow zone indicator method. That conclusion is represented by good matching between observed data and simulated results for all reservoir responses such for the artificial intelligence model than the flow zone indicator model.
Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in
... Show MoreFacial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f
... Show MoreTiO2 thin films were deposited by Spray Pyrolysis with thickness ((350±25) nm) onto glass substrates at (350°C), and the film was annealed at temperatures (400 and 500)°C. The structural and morphological properties of the thin films (TiO2) were investigated by X-ray diffraction, Field emission scanning electron microscopy and atomic force microscope. The gas sensor fabricated by evaporating aluminum electrodes using the annealed TiO2 thin films as an active material. The sensitivity of the sensors was determined by change the electrical resistance towards NO2 at different working temperatures (200
Hydrocarbon displacement at the pore scale is mainly controlled by the wetness properties of the porous media. Consequently, several techniques including nanofluid flooding were implemented to manipulate the wetting behavior of the pore space in oil reservoirs. This study thus focuses on monitoring the displacement of oil from artificial glass porous media, as a representative for sandstone reservoirs, before and after nanofluid flooding. Experiments were conducted at various temperatures (25 – 50° C), nanoparticles concentrations (0.001 – 0.05 wt% SiO2 NPs), salinity (0.1 – 2 wt% NaCl), and flooding time. Images were taken via a high-resolution microscopic camera and analyzed to investigate the displacement of the oil
... Show MoreIn this paper the definition of fuzzy normed space is recalled and its basic properties. Then the definition of fuzzy compact operator from fuzzy normed space into another fuzzy normed space is introduced after that the proof of an operator is fuzzy compact if and only if the image of any fuzzy bounded sequence contains a convergent subsequence is given. At this point the basic properties of the vector space FC(V,U)of all fuzzy compact linear operators are investigated such as when U is complete and the sequence ( ) of fuzzy compact operators converges to an operator T then T must be fuzzy compact. Furthermore we see that when T is a fuzzy compact operator and S is a fuzzy bounded operator then the composition TS and ST are fuzzy compact
... Show MoreRecently, important efforts have been made in an attempt to search for the cheapest and ecofriendly alternatives adsorbents. In the present work, waste molasses from Iraqi date palm (Zahdi) had been used as a provenance to produce charcoal for the removal of methylene blue (MB) dye from water. The optimum prepared charcoal was obtained at 150 C, by increasing temperature to 175 C, the charcoal had almost converted to ash. The obtained charcoal have been inspected for properties using scanning electron microscope (SEM), atomic force microscope (AFM), porosity and surface area. Adsorption data were optimized to Langmuir and Freundlich and adsorption parameters have been evaluated. The thermodynamic parameters like a change
... Show MoreThis paper studies the effect of mean wind velocity on tall building. Wind velocity, wind profile and wind pressure have been considered as a deterministic phenomenon. Wind velocity has been modelled as a half-sinusoidal wave. Three exposures have been studied B, C, and D. Wind pressure was evaluated by equation that joined wind pressure with mean wind velocity, air density, and drag coefficient.
Variations of dynamic load factor for building tip displacement and building base shear were studied for different building heights, different mode shapes, different terrain exposures, and different aspect ratios of building plan. SAP software, has been used in modelling and dynamic analysis for all case studies.
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