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

The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of

This study investigates the application of hydraulic acid fracturing to enhance oil production in the Mishrif Formation of the Al-Fakkah oilfield due to declining flow rates and wellhead pressures resulting from asphaltene deposition and inadequate permeability. Implementing acid fracturing, an established technique for low-permeability carbonate reserves, was essential due to the inadequacy of prior solvent cleaning and acidizing efforts. The document outlines the protocols established prior to and following the treatment, emphasizing the importance of careful oversight to guarantee safety and efficacy. In the MiniFrac treatment, 150 barrels of #30 cross-linked gel were injected at 25 barrels per minute, followed by an overflush wi

The calculation of the oil density is more complex due to a wide range of pressuresand temperatures, which are always determined by specific conditions, pressure andtemperature. Therefore, the calculations that depend on oil components are moreaccurate and easier in finding such kind of requirements. The analyses of twenty liveoil samples are utilized. The three parameters Peng Robinson equation of state istuned to get match between measured and calculated oil viscosity. The Lohrenz-Bray-Clark (LBC) viscosity calculation technique is adopted to calculate the viscosity of oilfrom the given composition, pressure and temperature for 20 samples. The tunedequation of state is used to generate oil viscosity values for a range of temperatu

This paper reports experimental and computational fluid dynamics (CFD) modelling studies to investigate the effect of the swirl intensity on the heat transfer characteristics of conventional and swirl impingement air jets at a constant nozzle-to-plate distance ( L = 2 D). The experiments were performed using classical twisted tape inserts in a nozzle jet with three twist ratios ( y = 2.93, 3.91, and 4.89) and Reynolds numbers that varied from 4000 to 16000. The results indicate that the radial uniformity of Nusselt number (Nu) of swirl impingement air jets (SIJ) depended on the values of the swirl intensity and the air Reynolds number. The results also revealed that the SIJ that was fitted with an insert of y = 4.89, which correspo

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences