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title

Application of Intelligent Models for Prediction of Solution Gas Oil Ratio

Credit to Download: 1 | Page Numbers 11 | Abstract Views: 637
Year: 2015
COI code: RESERVOIR05_005
Paper Language: English

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Authors Application of Intelligent Models for Prediction of Solution Gas Oil Ratio

  Seyed Morteza Tohidi Hosseini - Master Student of Production Engineering, Amirkabir University of Technolgy
  Sina Shahriari Moghaddam - Master Student of Petroleum Facilities, Amirkabir University of Technology
Babak Ahmadirad - Master Student of Reservoir Engineering, Oloom Tahqiaqat University
  Mehran Hashemi Doulatabadi - Bsc of Petroleum Engineering, Amirkabir University of Techonology

Abstract:

Accurate calculation of PVT properties is a basic requirement for petroleum engineering computations like reservoir simulation, material balance, and well-test. Experimental tests of PVT are time-consuming and costly. Therefore, prediction models for PVT properties such as bubble point pressure, dew point pressure and solution gas oil ratio have been developed using regression models. In this study, new intelligent models for solution gas oil ratio were developed using more than 1100 experimental data series. Two robust intelligent tools, namely adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network were used for development of the models. Precise comparison of the developed models with other published correlation showed that the developed models are superior to all other correlations. Comparison of the ANFIS model and ANN model showed that, ANFIS model is more accurate than ANN model and is the best model for calculation of solution gas oil ratio

Keywords:

solution gas oil ratio; crude oil; adaptive neuro-fuzzy inference system; artificial neural network

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COI code: RESERVOIR05_005

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Tohidi Hosseini, Seyed Morteza; Sina Shahriari Moghaddam; Babak Ahmadirad & Mehran Hashemi Doulatabadi, 2015, Application of Intelligent Models for Prediction of Solution Gas Oil Ratio, 5th conference of reservoir and upstream industries and related industries, تهران, شركت هم انديشان انرژي كيميا, https://www.civilica.com/Paper-RESERVOIR05-RESERVOIR05_005.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Tohidi Hosseini, Seyed Morteza; Sina Shahriari Moghaddam; Babak Ahmadirad & Mehran Hashemi Doulatabadi, 2015)
Second and more: (Tohidi Hosseini; Shahriari Moghaddam; Ahmadirad & Hashemi Doulatabadi, 2015)
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Type: state university
Paper No.: 19662
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