Research Paper
Petroleum Engineering
Yaser Ahmadi
Abstract
Recently nanoparticles are used for improving the volume of oil and gas production and Enhanced Oil Recovery (EOR) purposes. Based on our recent researches, using nanoparticles such as Silica and Calcium oxide has a good potential for changing mechanisms in the porous media such as interfacial tension ...
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Recently nanoparticles are used for improving the volume of oil and gas production and Enhanced Oil Recovery (EOR) purposes. Based on our recent researches, using nanoparticles such as Silica and Calcium oxide has a good potential for changing mechanisms in the porous media such as interfacial tension and wettability. For finding the application of nanoparticles in the porous media, low permeability carbonate plugs were selected, and two main steps were used , including 1) Using CaO and SiO2 nanoparticles for wettability alteration, interfacial tension reduction, and improving fluid flow through porous media 2) Surveying the application of nanoparticles on the water alternative gas (NCs assisted WAG) test. The Zeta potential amounts are stable at condition of -56.4±2 mV and -44.0±3 mV for Calcium oxide and Silica nanoparticles, respectively at optimum nanoparticles concentration of 15 ppm. Calcium oxide and Silica nanoparticles have effectively altered the wettability from oil-wet to water-wet by surveying the intersection of two-phase relative permeability. Moreover, CaO nanoparticles had better performance in low permeability carbonate porous media than SiO2 nanoparticles with regards to wettability alteration to water wet. Based on the results and better version of CaO, it was selected for performing NCs assisted WAG tests at WAG ratios of 1:1, 40 ℃, and 15 ppm.The recovery factor was increased from 42.9 % to 73 % in the presence of CaO during performing NCs assisted WAG tests, and residual oil saturation was decreased from 40.9 % to 19.4 %.
Research Paper
Mechanical Engineering
Hadi eskandari; Moslem Ghanbari
Abstract
The present study deals on the geometry effects of the spherical pressure vessel(SPV) and the crack configuration on the variation of the stress intensity factor (SIF) through the crack line. The pressurized vessel is subjected to the pressure and thermal gradient (thermo-mechanical loading). The 3D ...
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The present study deals on the geometry effects of the spherical pressure vessel(SPV) and the crack configuration on the variation of the stress intensity factor (SIF) through the crack line. The pressurized vessel is subjected to the pressure and thermal gradient (thermo-mechanical loading). The 3D analysis of defected thick-walled pressurized sphere vessel is done using the numerical Finite Element Method (FEM). This work covers a wide range of the crack configurations in vessels with different geometries. The effect of the various parameters such as thermal gradient, RO/Ri, a/c and a/t on the variation of the dominant first mode of SIF through the crack front is studied. The obtained SIF’s are compared with the mechanical loading results (in the absence of the thermal gradient). The results show that parameters such as the aspect ratio of the crack, the ratio of the crack depth, the pressure vessel wall thickness and also the temperature gradient have significant effects on the distribution of the SIF through the crack line. It can be seen that the thermo-mechanical condition is more critical.
Research Paper
Petroleum Engineering – Exploration
Mehrdad Safarpour; Mohammad Ali Riahi; Mehran Rahimi
Abstract
The main purpose of this paper is to estimate and evaluate the petrophysical properties of the Ghar Formation in the Hindijan and Bahregansar oilfields using a combination of seismic and well logs data. In this study, following a step-by-step regression approach: first; sonic, density, and, porosity ...
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The main purpose of this paper is to estimate and evaluate the petrophysical properties of the Ghar Formation in the Hindijan and Bahregansar oilfields using a combination of seismic and well logs data. In this study, following a step-by-step regression approach: first; sonic, density, and, porosity well-log data are collected. Second; seismic attributes, including amplitude, phase, frequency, and acoustic impedance are extracted from the seismic lines intersecting the wellbore locations. Then, using the MFLN and PNN intelligent systems, a relationship between porosity, shale volume, saturation, and seismic attributes is established. Using this relationship, the physical and petrophysical properties of the reservoir in the Ghar Formation are estimated and evaluated. We estimated the reservoir porosity between 15% and 20%, which is higher in the Hendijan oilfield as compared to the Bahregansar oilfield. The amount of water saturation in the Ghar formation varies between 25 and 30 percent. On the other hand, the amount of clay content and shale volume of the Ghar Formation in the Hendijan field is higher than that of the Bahregansar oil field.
Research Paper
Petroleum Engineering – Exploration
Hamid Reza Okhovvat; Mohammad Ali Riahi; Afshin Akbari Dehkharghani
Abstract
In this study, in order to facies classification, the kernel principal component analysis (KPCA) feature extraction method is used to extract new features from the measured well-logs. After applying the Principal Component Analysis (PCA), and KPCA feature extraction approaches, the classification was ...
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In this study, in order to facies classification, the kernel principal component analysis (KPCA) feature extraction method is used to extract new features from the measured well-logs. After applying the Principal Component Analysis (PCA), and KPCA feature extraction approaches, the classification was made using three powerful classifiers: Multilayer Perceptron Neural Network (MLP), Support Vector Machine (SVM), and Random Forest (RF). Finally, the predicted results for the test data that were not included in the training process were evaluated with the F1 score criterion.
The PCA method did not show a significant effect on the classification performance due to the nonlinear structure of the facies. Our results show that the KPCA improves the performance of facies classification. Compared with the conventional approach based on well-log data, our new approach improves the classification accuracy for each classifier algorithm. In the RF results, the classification accuracy has increased by about 6% while using the KPCA feature extraction approach, increasing from 52% to 58% compared to the original well-log data.
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