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مقاله Oilfield Production Optimization under Oil Price Uncertainty, By Nonlinear Programming and MonteدرCarlo Simulation با word دارای 7 صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است
فایل ورد مقاله Oilfield Production Optimization under Oil Price Uncertainty, By Nonlinear Programming and MonteدرCarlo Simulation با word کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه و مراکز دولتی می باشد.
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توجه : در صورت مشاهده بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی مقاله Oilfield Production Optimization under Oil Price Uncertainty, By Nonlinear Programming and MonteدرCarlo Simulation با word،به هیچ وجه بهم ریختگی وجود ندارد
بخشی از متن مقاله Oilfield Production Optimization under Oil Price Uncertainty, By Nonlinear Programming and MonteدرCarlo Simulation با word :
سال انتشار: 1391
محل انتشار: هشتمین کنفرانس بین المللی مهندسی صنایع
تعداد صفحات: 7
چکیده:
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.
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