List of Articles تراوایی

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  1 - Numerical calculation of permeability tensor in fractured reservoirs
  سیما جلیلی رئوف حسین معماریان محمد رضا  رسائی بهزاد تخم چی
  Abstract Proper characterization of fracture reservoir is crucial for their sound development plan. It is however very difficult to correctly obtain various fracture reservoir properties such as permeability due to high order of heterogeneity and anisotropy within th More

  Abstract Proper characterization of fracture reservoir is crucial for their sound development plan. It is however very difficult to correctly obtain various fracture reservoir properties such as permeability due to high order of heterogeneity and anisotropy within these reservoirs. Classical dual porosity and/or dual permeability models consider a regular fracture network across the reservoir. To improve the concept, we develop a numerical method for tonsorial permeability calculation of blocks with random/disordered fracture distribution. We considered a 2D Cartesian fine grid in which the fractures were defined explicitly with their endpoints coordinates. Applying proper boundary conditions, single phase flow is then solved. Full tensor permeability is then obtained analytically from the calculated flow and pressure fields. The result of our method is compared well with that of the analytical models for simple fracture systems. In addition we reported the permeability tensor values of random fracture networks where no analytical solution is available. Manuscript profile
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  2 - Estimation of relative permeability curves from capillary pressure data in one of iranian carbonate reservoir
  بابک شعبانی عزت اله کاظم زاده
  20.1001.1.22518738.1390.1.2.1.1
  Relative permeability can be measured directly from cores, but due to problems such as unavailability of experimental results of direct relative permeability measurement, indirect techniques also have been used to calculate relative permeability. One of these methods is More

  Relative permeability can be measured directly from cores, but due to problems such as unavailability of experimental results of direct relative permeability measurement, indirect techniques also have been used to calculate relative permeability. One of these methods is estimating relative permeability curves from capillary pressure data that the reliability of this method for approximation of liquid-gas relative permeability curves had thoroughly investigated. However, there is not enough information to conclude which method is the standard one for calculating oil-water relative permeability curves. Various capillary pressure techniques such as the Corey, Brooks-Corey, Li-Purcell and Li-Burdine methods were utilized to calculate oil-water relative permeabilities using the measured oil-water capillary pressure data in drainage process in an oil-wet Carbonate reservoir. Despite wide popularity of Purcell and Burdine methods for calculating relative permeability, new Li-Purcell and LiBurdine methods were used. The calculated results were compared to the experimental data of oil-water relative permeabilities measured in a Carbonate reservoir. The Corey and Brooks-Corey models are shown an acceptable and nearly exact match with the measured oil relative permeability values. However, the Li-Purcell and Li-Burdine models underestimate the values for wetting phase in most cases. It is also worth mentioning that, except Li-Purcell method, the results of all other methods for calculating non-wetting phase relative permeability are almost the same and overestimate the values. Then, rock typing on the basis of pore throat radius at 35% mercury saturation were done and the accuracy of each model were examined for all rock types. Results of this work revealed that calculation of oil-water relative permeability using the capillary pressure data is also a reliable technique in oil-wet carbonate reservoirs. Manuscript profile
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  3 - Comparisons of intelligent systems and empirical equation results in permeability prediction: a case study in one of the southern Iranian carbonate reservoirs
  الهام عزیز ابادی فراهانی مجتبی رجبی
  Prediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The mo More

  Prediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The most reliable data of permeability are taken from laboratory analysis of cores. Since coring is a costly and time consuming operation, researchers have tried to predict this parameter from other methods. Empirical equation is one of these methods, but results of these equations are not satisfied for all lithology and reservoirs. So far, several studies have been carried out for the estimation of reservoir parameters using intelligent systems. These studies indicate the successful role of these methods such as fuzzy logic, neuro-fuzzy and genetic algorithms for reservoir characterization. In this study, we try to compare results of these two methods (empirical equations and intelligent systems) for permeability prediction in a carbonate reservoir. For this purpose, petrophysical and core data of four well in a carbonate reservoir in the Southern Iran were used. At first, using empirical equations permeability was calculated for the test well; then using data of three wells, intelligent models were constructed. A forth well (test well) from the field was used to evaluate the models. The results show that fuzzy logic result (with R2= 0.88) is the best method for prediction of permeability in the studied reservoir. Also between empirical equations, result of Wyllie-Rose equation is better than others. Finally we offer the constructed fuzzy model (as a best predictor) for permeability prediction in the studied reservoir. Manuscript profile
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  4 - Comparisons of intelligent systems and empirical equation results in permeability prediction: a case study in one of the southern Iranian carbonate reservoirs
  Amir Mola jan Hoseyn Memarian
  20.1001.1.22518738.1390.1.1.1.9
  Prediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The mo More

  Prediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The most reliable data of permeability are taken from laboratory analysis of cores. Since coring is a costly and time consuming operation, researchers have tried to predict this parameter from other methods. Empirical equation is one of these methods, but results of these equations are not satisfied for all lithology and reservoirs. So far, several studies have been carried out for the estimation of reservoir parameters using intelligent systems. These studies indicate the successful role of these methods such as fuzzy logic, neuro-fuzzy and genetic algorithms for reservoir characterization. In this study, we try to compare results of these two methods (empirical equations and intelligent systems) for permeability prediction in a carbonate reservoir. For this purpose, petrophysical and core data of four well in a carbonate reservoir in the Southern Iran were used. At first, using empirical equations permeability was calculated for the test well; then using data of three wells, intelligent models were constructed. A forth well (test well) from the field was used to evaluate the models. The results show that fuzzy logic result (with R2= 0.88) is the best method for prediction of permeability in the studied reservoir. Also between empirical equations, result of Wyllie-Rose equation is better than others. Finally we offer the constructed fuzzy model (as a best predictor) for permeability prediction in the studied reservoir. Manuscript profile
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  5 - Comparisons of intelligent systems and empirical equation results in permeability prediction: a case study in one of the southern Iranian carbonate reservoirs
  الهام عزیز ابادی فراهانی Kazemzadeh Ezatolah ELham Aziz Abadi Farahani
  Prediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The mo More

  Prediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The most reliable data of permeability are taken from laboratory analysis of cores. Since coring is a costly and time consuming operation, researchers have tried to predict this parameter from other methods. Empirical equation is one of these methods, but results of these equations are not satisfied for all lithology and reservoirs. So far, several studies have been carried out for the estimation of reservoir parameters using intelligent systems. These studies indicate the successful role of these methods such as fuzzy logic, neuro-fuzzy and genetic algorithms for reservoir characterization. In this study, we try to compare results of these two methods (empirical equations and intelligent systems) for permeability prediction in a carbonate reservoir. For this purpose, petrophysical and core data of four well in a carbonate reservoir in the Southern Iran were used. At first, using empirical equations permeability was calculated for the test well; then using data of three wells, intelligent models were constructed. A forth well (test well) from the field was used to evaluate the models. The results show that fuzzy logic result (with R2= 0.88) is the best method for prediction of permeability in the studied reservoir. Also between empirical equations, result of Wyllie-Rose equation is better than others. Finally we offer the constructed fuzzy model (as a best predictor) for permeability prediction in the studied reservoir. Manuscript profile
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  6 - Simulation of porosity and permeability reservoir parameters by using Co-Sequential Gaussian Simulation method in one of the oil field in the South West of Iran
  Bahareh Fereidooni Mohammad Mokhtari
  20.1001.1.22518738.1396.7.13.3.7
  Three-dimensional study of petrophysical parameters of hydrocarbon reservoirs such as porosity and permeability is considered as an efficient and effective tool for comprehensive study of reservoirs as well as reservoir management. In this study, which was carried out o More

  Three-dimensional study of petrophysical parameters of hydrocarbon reservoirs such as porosity and permeability is considered as an efficient and effective tool for comprehensive study of reservoirs as well as reservoir management. In this study, which was carried out on one of the oil fields in the southwest of Iran, the aim is to simulate the petrophysical parameters of effective porosity and permeability by using Co-Sequential Gaussian Simulation in part of Khatiyah reservoir. With this simulation, a three-dimensional model of petrophysical reservoir parameters can be presented which is important for simulating fluid flow and identifying areas that are prone with higher reservoir quality. For this purpose, effective porosity and permeability logs of seven wells with 3D seismic data and seismic inversion results have been used. After reservoir gridding and creating a structural model, up scaled petrophysical data has entered to model and its own cell. For three-dimensional distribution of effective porosity parameter, due to the correlation of effective porosity and acoustic impedance attribute of seismic inversion, 3D seismic data and up scaled effective porosity logs as the initial data and acoustic impedance attribute of seismic inversion as secondary data have entered in Sequential Gaussian Simulation. In order to simulate permeability, due to the good correlation between effective porosity simulation model and permeability log, simulated porosity as a secondary data and up scaled permeability and 3D seismic data as secondary data have been used. The results of validation indicate the accuracy of the present study and the efficiency of Sequential Gaussian Simulation method in effective porosity and permeability modeling in this reservoir. Manuscript profile
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  7 - A case study of carbonate reservoir permeability determination using NMR log in one of the southwestern fields of Iran
    Bahram Habibnia
  20.1001.1.22518738.1396.7.13.4.8
  Permeability is one of the most important parameters for characterization of hydrocarbon reservoirs that has a basic role in almost all of the petroleum engineering problems. Determination of reservoir permeability is usually done in core laboratories in a time consumin More

  Permeability is one of the most important parameters for characterization of hydrocarbon reservoirs that has a basic role in almost all of the petroleum engineering problems. Determination of reservoir permeability is usually done in core laboratories in a time consuming process. In the well test, the obtained average permeability is related to the drainage area. Due to cost, these two methods are not performed in the all wells, whereas well logging tools are generally performed in all wells. With progress of well logging tools, some researchers tried to estimate permeability from special well logs such as NMR directly. The data obtained from NMR was used as lithology independent data to estimate the water saturation and porosity, and also for analysis of pore space. One of the important parameters obtained from NMR is the transversal relaxation time (T2). In this work, NMR log measurement in a carbonate field was used to estimate the permeability using Timur, SDR and regression models. The results of the methods were compared against core permeability. The results show that SDR method is more accurate with the accuracy of 44.1% and the error of 23.12%. Manuscript profile
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  8 - Permeability estimation using petrophysical logs and artificial intelligence methods: A case study in the Asmari reservoir of Ahvaz oil field
  Abouzar Mohsenipour Bahman Soleimani iman Zahmatkesh Iman  Veisi
  20.1001.1.22518738.1399.10.20.4.3
  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calcula More

  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calculated using two conventional methods, free fluid model (Coates) and Schlumberger model or mean T2 (SDR). Then, by constructing a simple model of artificial neural network and also combining it with Imperialist competition optimization (ANN-ICA) and particle swarm (ANN-PSO) algorithms, the permeability was estimated. Finally, the results were compared by comparing the estimated COATES permeability and SDR permeability with the actual value, and the estimation accuracy was compared in terms of total squared error and correlation coefficient. The results of this study showed an increase in the accuracy of permeability estimation using a combination of optimization algorithms with artificial neural network. The results of this method can be used as a powerful method to obtain other petrophysical parameters. Manuscript profile
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  9 - Permeability improvement calculated from Stoneley-FZI method in Kangan reservoir, one of Iran's gas fields
  hossein rezaie yegane doost
  10.61186/ispg.38526.11.22.39
  20.1001.1.22518738.1400.11.22.2.2
  Permeability in fluid flow is for a porous rock, which is exactly what causes the problem. core analysis and well testing are two most commonly used methods of permeability measurement, but in-vitro measurement of permeability by applying core analysis on all wells in a More

  Permeability in fluid flow is for a porous rock, which is exactly what causes the problem. core analysis and well testing are two most commonly used methods of permeability measurement, but in-vitro measurement of permeability by applying core analysis on all wells in a specific field is very time consuming and costly and even impossible when dealing with Horizontal wells. Wells testing, on the other hand, is not cost-effective for reasons such as; High costs and zero production during the testing process. Therefore, thanks to their low cost, comprehensiveness and availability, permeability estimation methods developed according to conventional logs land DSI diagrams are of critical importance. Taking this into account, in the present study, permeability was first estimated using multi-resolution graph-based clustering (MRGC) and the results were compared with permeability rates obtained from core analysis. In the second stage, permeability was measured by ST-FZI method and the results were compared with permeability rates obtained from core analysis. In the third stage, the multi-resolution graph-based clustering (MRGC) method was used to improve the permeability calculated by the ST-FZI method and overcome the reservoir heterogeneity. First the flow units were identified, and then the ST-FZI method was applied on each flow unit to calculate permeability and finally the calculated permeabilities were combined to obtain an accurate permeability graph of the studied well. The correlation coefficients of permeability rates estimated via core analysis in the multi-resolution graph-based clustering method (R2 = 77), ST-FZI method (R2 = 47) and improved method (R2 = 84) were measured. The afore-mentioned method was able to improve the permeability calculated in the previous step by 37% and was recognized as the best permeability measurement method in the Kangan reservoir of the well subjected to study. Manuscript profile