توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Optimization of Injection and Withdrawal Rate and Pressure of Depleted Gas Condensate Reservoir for Storage of Natural Gas in Iran
نویسنده(ها): ،
اطلاعات انتشار: اولین کنگره مهندسی نفت ایران، سال
تعداد صفحات: ۳۳
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۲Comparision Study of Published PVT Correlations and its Application to Estimate Reservoir Fluid Properties for Iranian Oil Rerservoir
نویسنده(ها): ، ،
اطلاعات انتشار: دوازدهمین کنگره ملی مهندسی شیمی ایران، سال
تعداد صفحات: ۱۰
Bubble point pressure, gas oil ratio, viscosity and oil formation volume factor are primary importance in material balance calculations, analysis of well performance, and etc.. Standing proposed a correlation for determining the formation volume factor and bubble point pressure. A total of 105 experimentally determined data points on 22 different crude oil gas mixtures from California fields were utilized in arriving at the correlation. The reliability of the literature correlations was tested in order to evaluate their performance in dealing with Iranian oils and to determine the most suitable correlation for bubble point pressure, GOR, viscosity and oil FVF. A qualitative analysis was carried out using the standard error of estimate, the average percent relative error,and etc.. Even though the Standing and Al–Marhoun correlations were found to be accurate to certain extent comparing with other correlations, they were in general not suitable to be used in estimation of the above mentioned Iranian oil properties.<\div>

۳A New Correlation For Predicting Hydrate formation temperature Using Artificial neural network
اطلاعات انتشار: اولین همایش ملی توسعه تکنولوژی در صنایع نفت، گاز و پتروشیمی، سال
تعداد صفحات: ۴
Gas hydrates are a costly problem when they plug oil and gas pipelines. The best way to determine the Hydrate Formation Temperature (HFT) and pressure is to measure these conditions experimentally for every gas system. Since this is not practical in terms of time and money, correlations are the other alternative tools. There are a few numbers of correlations for specific gravity method to predict the hydrate formation. As the hydrate formation temperature is a function of pressure and gas gravity, an empirical correlation is presented based on the Hammerschmidt correlation for predicting the hydrate formation temperature. In order to obtain a new proposed correlation, 357 experimental data points have been collected from gas–gravity curves. This correlation is programmed and assessed with respect to its capabilities to match experimental data published in the literature under varying system conditions (i.e. temperature, pressure, and composition). The LINGO software has been employed for statistical analysis of the data. Accuracy of our correlation is more accurate than the Hammerschmidt correlation. In order to establish a method to predict the hydrate formation temperature, a new neural network has also been developed with the BP (back propagation) method. This neural network (IPS) model enables the user to accurately predict hydrate formation conditions for a given gas mixture, without having to do costly experimental measurements. It is found that the IPS neural network and the AUT correlation have the same results and are more accurate than the empirical correlation.<\div>

۴Oilfield Production Optimization under Oil Price Uncertainty, By Nonlinear Programming and Monte–Carlo Simulation
نویسنده(ها): ،
اطلاعات انتشار: هشتمین کنفرانس بین المللی مهندسی صنایع، سال
تعداد صفحات: ۷
One of the most challenging problems in production operations is gas lift allocation optimization to the wells in such a way that maximizes total oil production benefit of thefield. In literature, this problem is solved in different ways without considering all real constraints. Also, in practice oil price is not acertain parameter. Therefore, present study describes a nonlinear programming approach to maximize daily cash flow of some gas–lifted wellsin an uncertain condition for oil price. Firstly, solution points of each well are obtained by employing a production simulation software and asurrogate model is developed for gas lift performance in each well by use of nonlinear optimization. Then these functions are used to develop a model under capacity, pressure andother real constraints for the production cash flow. Oil price is assumed as a triangle risk function in this model. Results show a significantincrease in cash flow in comparison with old case due to appropriate gas injection parameters. Sensitivity analysis on this problem shows that oilprice, compression cost and water oil ratio variations should be considered in the long–term optimization.<\div>

۵Using a Proxy Model Network to Predict Liquid Loading
نویسنده(ها): ،
اطلاعات انتشار: سومین کنگره ملی مهندسی نفت، سال
تعداد صفحات: ۱۲
The gas well loading phenomenon is one of the most serious problems that reduces, and eventually cuts, production in gas wells. This phenomenon occurs as a result of liquid accumulation of condensate inthe well bore. Over time, these liquids cause additional hydrostatic backpressure on the reservoir which results in a continual reduction of the available transport energy. The well therefore starts slugging whichgives an even larger chance of liquid accumulation that completelyovercomes the reservoir pressure and causes the well to die [1]. Typical solutions of loading phenomenon were to unload the well artificially; either mechanically using pumps or with gas lift kicking with nitrogen through coiled tubing. However, in addition to the expense and loss of production, artificial lift solutions remain temporary and the well is subject to reloading again. Therefore, thought was directed toward developing some solutions that enable the well to continuously unload itself without the aid of external help unloading operations [2].<\div>

۶A new correlationfor predicting hydrate formation temperature
نویسنده(ها): ،
اطلاعات انتشار: سومین کنگره ملی مهندسی نفت، سال
تعداد صفحات: ۱۶
Gas hydrates (gas clathrates) are solid compounds of natural gas molecules that are encaged within a crystal structure composed of water molecules. Hydrates can form anywhere and anytime that hydrocarbons and water are present at low temperatures and high pressures (Sloan,1999). The formation of gas hydrates during hydrocarbon production and transportation is a serious problem in the petroleum industry.Typical hydrate–forming guests include CH4, C2H6, C3H8, i–C4H10, CO2, H2S, CHCL3 and the noble gases. (Sloan,1998)Gas hydrate formation is a concomitant process requiring the presence of both the host and guest molecular species. Conditions under which hydrates will form are determined largely by the nature of the guest, but for most common compounds of natural gas they will crystallize at temperatures above the ice point with pressures nearly 10 atm. Such conditions are also common in oil and gas transmission, and so gas hydrate formation is a major potential cause of pipeline occlusion. The existence of clathrate hydrate was first documented by Sir Humphery Davy in 1810, who observed that a solution of chlorine gas in water freezes more readily than pure water. Since 1934, when Hummerschmidt concluded that natural gas hydrates were blocking gas transmission lines, the susceptibility of forming solid hydrates in gas transmission under normal operating conditions has led to many investigations aimed at understanding and avoiding hydrate formation, an area of ongoing research (Sloan,1998).<\div>

۷An Empirical Correlation to Predict Liquid Loading
نویسنده(ها): ،
اطلاعات انتشار: سومین کنگره ملی مهندسی نفت، سال
تعداد صفحات: ۸
The gas well loading phenomenon is one of the most serious problems that reduces, and eventually cuts, production in gas wells. This phenomenon occurs as a result of liquid accumulation either water and\or condensate in the well bore. Over time, these liquids cause additional hydrostatic backpressure on the reservoir which results in a continual reduction of the available transport energy. The well therefore starts slugging which gives an even larger chance of liquid accumulation that completely overcomes the reservoir pressure and causes the well to die.the development of the loading phenomenon in a gas well. Typical solutions were to unload the well artificially, either mechanically using pumps or with gas lift kicking with nitrogen through coiled tubing. However, in addition to the expense and loss of production, artificial lift solutions remain temporary and the well is subject to reloading again.Therefore, thought was directed toward developing some solu– tions that enable the well to continuously unload itself without the aid of external help unloading operations [1,3].<\div>

۸Controlling the Genetic Algorithm Parameters by Binding It to Simulated Annealing (Case study: Petroleum Engineering)
نویسنده(ها): ،
اطلاعات انتشار: کنفرانس بین المللی یافته های نوین پژوهشی در مهندسی صنایع و مهندسی مکانیک، سال
تعداد صفحات: ۹
One of the most common optimization algorithms is genetic algorithm which is used is different problems. There are some internal parameters for the genetic algorithm that changing them alters the application of the algorithm. To find the best optimizer’s parameters, it is usual to change one parameter and set other ones to a constant value, and again change another parameter and set others to a fixed value. This method needs the different runs of the optimizer (with different optimizer parameters) and it is clear that is very time consuming. Here for this purpose the genetic algorithm is coupled with simulated annealing. Thus, genetic algorithm optimizes the problem and simultaneously simulated annealing optimizes the parameters of the genetic algorithm. Afterward its application tested in a petroleum engineering problem. In some oil wells, gas is injected at the bottom of oil wells to bring the oil to the surface. This operation is called gas lift. Usually in gas lift operation there is a limited amount of gas that should be allocated between some wells in a way that the total produced oil be maximized. Here the genetic algorithm coupled with simulated annealing was used to find the best gas allocation which maximizes the oil production. Results show that this new mean is much faster than changing variable method (as previously mentioned) in addition to it, the quality of its optimum point is much better than other methods (changing variable method).<\div>

۹Selection of the Best Efficient Method for Natural Gas Storage at High Capacities Using TOPSIS Method
اطلاعات انتشار: Gas Processing Journal، اول،شماره۱، ۲۰۱۳، سال
تعداد صفحات: ۱۰
Nowadays one of the most important energy sources is natural gas. By depletion of oil reservoirs in the world, natural gas will emerge as the future energy source for human life. One of the major concerns of gas suppliers is being able to supply this source of energy the entire year. This concern intensifies during more consuming seasons of the year when the demand for natural gas increases, resulting in a lot of problems such as pressure depletion in the pipelines. One of the most effective policies to prevent pressure depletion is gas storage in warm seasons of the year when public demand is low. In this paper three different methods of underground and surface gas storage at high capacities have been discussed which are as follows: depleted oil and gas reservoirs, liquefied gas storage, and gas hydrates storage. In this study, the NPV function for economical evaluation of these three natural gas storage methods was employed. Finally, after assessing the technical and economical aspects of these methods, the TOPSIS model was constructed and depleted oil and gas reservoirs storage selected as the best natural gas storage method at high capacities.

۱۰Predicting the Hydrate Formation Temperature by a New Correlation and Neural Network
اطلاعات انتشار: Gas Processing Journal، اول،شماره۱، ۲۰۱۳، سال
تعداد صفحات: ۱۰
Gas hydrates are a costly problem when they plug oil and gas pipelines. The best way to determine the HFT and pressure is to measure these conditions experimentally for every gas system. Since this is not practical in terms of time and money, correlations are the other alternative tools. There are a small number of correlations for specific gravity method to predict the hydrate formation. As the hydrate formation temperature is a function of pressure and gas gravity, an empirical correlation is presented for predicting the hydrate formation temperature. In order to obtain a new proposed correlation, 356 experimental data points have been collected from gas–gravity curves. This correlation is programmed and assessed with respect to its capabilities to match experimental data published in the literature under varying system conditions (i.e. temperature, pressure, and composition).The SPSS software has been employed for statistical analysis of the data. In order to establish a method to predict the hydrate formation temperature, a new neural network has also been developed with the BP(Back Propagation) method. This neural network model enables the user to accurately predict hydrate formation conditions for a given gas mixture, without having to do costly experimental measurements.
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