Comparing Two Methods of Neural Networks to Evaluate Dead Oil Viscosity
نویسندگان
چکیده مقاله:
Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm) were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.
منابع مشابه
specialized methods to teach spelling: comparing three methods
چکیده: بررسی ادبیات مربوطه در کشور در زمینه یادگیری زبان انگلیسی نشان میدهد که علیرغم اهمیت املا در فرآیند یادگیری به طور عام و یادگیری زبان انگلیسی به طور خاص، این مولفه از جایگاهی متناسب با اهمیت آن برخوردار نیست و عمدتاً نادیده گرفته شده است. تحقیقات گستردهای در خارج از کشور در مورد ماهیت این مولفه صورت گرفته است، در حالی که به جرأت میتوان گفت در داخل کشور گامی در مورد درک ماهیت آن و فرآی...
15 صفحه اولUsing Intelligent Methods and Optimization of the Existing Empirical Correlations for Iranian Dead Oil Viscosity
Numerous empirical correlations exist for the estimation of crude oil viscosities. Most of these correlations are not based on the experimental and field data from Iranian geological zone. In this study several well-known empirical correlations including Beal, Beggs, Glasso, Labedi, Schmidt, Alikhan and Naseri were optimized and refitted with the Iranian oil field data. The results showed that ...
متن کاملusing intelligent methods and optimization of the existing empirical correlations for iranian dead oil viscosity
numerous empirical correlations exist for the estimation of crude oil viscosities. most of these correlations are not based on the experimental and field data from iranian geological zone. in this study several well-known empirical correlations including beal, beggs, glasso, labedi, schmidt, alikhan and naseri were optimized and refitted with the iranian oil field data. the results showed that ...
متن کاملComparing Methods to Extract the Knowledge from Neural Networks
Neural networks (NN) have been shown to be accurate classifiers in many domains. Unfortunately, the lack of NN’s explanatory capability of knowledge learned has somewhat limited their application. A stream of research has therefore developed focusing on knowledge extraction from within neural networks. The literature, unfortunately, lacks consensus on how best to extract knowledge from help neu...
متن کاملViscosity Reduction of Heavy Crude Oil by Dilution Methods: New Correlations for the Prediction of the Kinematic Viscosity of Blends
Dilution is one of the various existing methods in reducing heavy crude oil viscosity. In this method, heavy crude oil is mixed with a solvent or lighter oil in order to achieve a certain viscosity. Thus, precise mixing rules are needed to estimate the viscosity of blend. In this work, new empirical models are developed for the calculation of the kinematic viscosity of crude oil and diluent ble...
متن کاملPrediction of Kinematic Viscosity of Petroleum Fractions Using Artificial Neural Networks
In this work, artificial neural network (ANN) was utilized to develop a new model for the prediction of the kinematic viscosity of petroleum fractions. This model was generated as a function of temperature (T), normal boiling point temperature (Tb), and specific gravity (S). In order to develop the new model, different architectures of feed-forward type were examined. Finally, the optimum struc...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 7 شماره 1
صفحات 60- 69
تاریخ انتشار 2018-01-01
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023