Journal of Oil, Gas and Petrochemical Technology
سال نهم شماره 1 (Winter and Spring 2022)

In this study, the influence of line speed on the crystallinity of linear low-density polyethylene (LLDPE) stretch films manufactured in a cast extrusion line was examined using differential scanning calorimetry (DSC) and wide-angle X-ray diffraction (WAXD). The multilayer LLDPE films were prepared at a wide range of line speeds. The DSC results showed that there was an increase in the crystallinity of films at higher line speeds. The crystallinity increased from 24.5 to 39.8 % while the line speed changed from 200 to 1000 m/min. Evaluating melting endotherms showed that the size of crystals was more uniform as the line speed increased. The crystallinity of films obtained from WAXD analysis exhibited the similar trend of DSC results, though their values were different. Additionally, there was a reduction in the crystal size calculated from WAXD data upon increasing the line speed. The observed increase in crystallinity and decrease in crystal size were due to the enhanced flow induced crystallization (FIC) as a result of greater shear stresses the polymer melt encountered at higher line speeds.

In the oil and gas industry, different kinds of nanoparticles have been studied and reported to enhance various oil and gas operations. In this review however, the focus is on identifying different applications of aluminum oxide (Al2O3) nanoparticles in the petroleum industry because of its various potentials. Al2O3 nanoparticle has been reported to be a good enhanced oil recovery agent that can alter rock wettability, reduce oil viscosity and reduce interfacial tension between oil and water. Al2O3 nanoparticle militates against kaolinite mobilization during hydrocarbon production, improves rheological properties in drilling fluids, improves the quality of cementation during well installations, and improves engine performance and thermal efficiency in brakes. Al2O3 nanoparticle has the capacity to inhibit growth of microbes, it is a catalyst in crude oil refining, it eliminates asphaltene from crude oil and is used to detect water oil interface. In biodiesel, Al2O3 nanoparticle reduces emissions and enhances fuel consumption.

The temperature- and concentration-dependent heat and mass diffusivities of a solute-solvent system are computed using an optimization-based computational technique. The input data of this method is the measured transient temperature and concentration at some selected locations of the system. The element-wise differential quadrature method as an accurate and simple numerical technique in conjunction with the Newton-Raphson method were utilized to solve the corresponding nonlinear coupled differential equations. The objective function of the algorithm is the difference between the measured data and the numerical solutions of the heat and mass transfer governing equations. The optimization algorithm is developed using the conjugate gradient method (CGM). Also, the corresponding nonlinear coupled partial differential equations are solved by employing the element-wise differential quadrature method as a powerful numerical technique. The applicability and reliability of the approach are illustrated by solving the problem under different conditions. The results showed that the heat and mass diffusivities of the system could be satisfactory estimated, which enable us to advise the application of this algorithm for the other transport phenomena.

In order to determine a suitable alternative welding method for substituation of fusion and resistance welding in oil and gas industry, a new method of projection friction stir spot welding with constant diameter and depth of protrusion is herein used for welding of 2024 aluminum sheets. By studying the previous research that was done on the friction stir spot welding of this material, the rotational speed of 800rpm is chosen for this purpose.For this purpose, the microstructure along with corrosion behavior of projection friction stir spot welding of Al2024 joints is investigated as a novel welding technique in oil and gas sector which produces reliable, safe, smooth and keyhole-free joints. The microstructural changes of different welding areas are studied by novel field emission scanning electron microscopy and consequently the hardness profiles of different areas is drawn. The effect of corrosive oil solution containing salts on welded specimens is studied and various types of pitting and intergranular corrosion are observed in different welding areas. Eventually, accumulation of corrosion products are observed in the stirred area of the weld and the TMAZ shows the highest rate of intergranular corrosion.

Pour point as an important physical property of crude oil is a measure of its low temperature fluidity. The accurate determination of this property is of significance as the temperature decrease below the pour point of the crude oil causes severe production and transportation problems. In this study, for the first time, two types of artificial neural networks (ANNs), including multilayer perceptron (MLP) and radial basis function (RBF), were proposed to predict the pour point. First, the MLP network was modeled and evaluated using different methods. To this end, the optimal number of the input parameters and the best activation function were examined. The results showed that the best predictive MLP network model is constructed using two parameters of wax content and cloud point and SoftMax-SoftMax activation function. The regression of training, validation, and testing datasets of the constructed MLP were 79.5%, 74.1%, and 76.8%, respectively. To validate the constructed MLP-ANN, some experiments were conducted on an oil sample. The dataset obtained from the experiments was then used to predict the pour point. The absolute error of 0.94 °C indicated the great performance of the MLP-ANN in predicting the pour point. Finally, the prediction performance of the MLP-ANN was compared to the RBF-ANN. The results showed the higher predictive accuracy of the MLP-ANN in comparison with the RBF-ANN. Based on the obtained results, the proposed MLP-ANN can be used with confidence in lieu of the expensive and time-consuming laboratory measurements to determine the pour point of crude oils.