کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4943063 | 1437619 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hybrid functional networks for oil reservoir PVT characterisation
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Hybrid functional networks for oil reservoir PVT characterisation Hybrid functional networks for oil reservoir PVT characterisation](/preview/png/4943063.png)
چکیده انگلیسی
Predicting pressure-volume-temperature (PVT) properties of black oil is one of the key processes required in a successful oil exploration. As crude oils from different regions have different properties, some researchers have used API gravity, which is used to classify crude oils, to develop different empirical correlations for different classes of black oils. However, this manual grouping may not necessarily result in correlations that appropriately capture the uncertainties in the black oils. This paper proposes intelligent clustering to group black oils before passing the clusters as inputs to the functional networks for prediction. This hybrid process gives better performance than the empirical correlations, standalone functional networks and neural network predictions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 87, 30 November 2017, Pages 363-369
Journal: Expert Systems with Applications - Volume 87, 30 November 2017, Pages 363-369
نویسندگان
Munirudeen A. Oloso, Mohamed G. Hassan, Mohamed B. Bader-El-Den, James M. Buick,