Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability. Trend test was performed to ensure that the developed model would follow the physical laws. Results show that the developed model outperforms the published correlations in term of absolute average percent relative error of 6.5%, and correlation coefficient of 96%.
In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H
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Soil defilement with "raw petroleum" is a standout amongst the most across the board and genuine ecological issues going up against both the industrialized and oil country like Iraq. Along these lines, the impact of "raw petroleum" on soil contamination is one of most critical subjects that review these days. The present examination expects to research "unrefined oil"effectson the mechanical and physical properties of clayey soils. The dirt examples were acquired from Al-Doura area in Baghdad city and arranged by the "Brought together Soil Grouping Framework (USCS)" as silty mud of low pliancy (CL). Research center tests were done on contaminated and unpolluted soil tests with same thickness. The dirtied tests are set up by blending
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Nanomaterials, including nanoparticles such as iron oxide nanoparticles, have received great attention from researchers due to their unique properties and applications. There are several diverse methods, including chemical, physical, and green biological methods, to prepare iron oxide nanoparticles. The green method was chosen because it is safer, purer, and less toxic compared to other methods. Therefore, the green method is a promising and environmentally friendly method in the near future. The aqueous extract of Iraqi orange leaves was used to prepare nano iron oxide, it was examined structurally and spectrally by several techniques (X-ray diffraction- XRD, Fourier transform infrared - FT-IR, field emission scanning electron micr
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Histologic changes were studied and physiological dosage crude alcoholic extract of seeds of the fenugreek plant for male mice eggs in different concentrations after oral to study testicular tissue and culverts where reason Abstract significant decrease
The Mishrif reservoir (Cenomanian - Turonian) in the Z, H, B and N oilfields in southern Iraq was investigated to clarify how nickel, vanadium, asphaltene, NSO and sulfur content affect the crude oil quality. The GC-Mass and ICP-MS analyses were used to provide fruitful hydrocarbon results. Classification of crude oil based on API gravity broadly indicates the oil's density and general properties. Typically, lighter crude oils are easier to refine, yield higher percentages of valuable products such as gasoline and diesel, and have a higher market value. Heavier crude oils require more processing and may yield more residual products, such as heavy fuel oil and asphalt. The Mishrif crude oil was classified as a medium sour crude oil c
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These dust designed to identify the extent of the impact of alcohol Almstkhalss saponin from fenugreek seeds on fertility in male mice eggs by tracking some physiological changes and tela that may occur to some members of the device Altcatherthe
A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.
Cervical carcinoma represent the second predominant cancer in female and there is a strong correlation between cervical cancer and the infection with high-risk types of HPV and expression the viral oncogenes. EMT is viewed as a vital advance in carcinoma development and ensuing metastasis. To evaluate correlation between the expression of Twist and HPV16 infection in a group of Iraqi patients with cervical carcinoma. A total of forty paraffin blocks included in this study which were divided into 30 sample of cervical cancer infected with HPV16and 10 sample of normal cervical tissues. The samples were subjected to immunohistochemical technique using Anti-Twist2 polyclonal antibody. The obtained data from this study indicate that majority of
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The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre
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