A process neural network model for calculation of heavy oil viscosity in high water cut stage. (16th February 2018)

• Record Type: Journal Article
• Authors: Pan, Baozhi; Zhu, Yunfeng; Wang, Chanjuan; Su, Siyuan
• Is Part Of: Petroleum science and technology. Volume 36:Number 4(2018)
• Page Start: 313
• Page End: 318
• Publication Date: 2018-02-16
• Subjects: heavy oil viscosity -- temperature -- high water cut stage -- API, models; Liquid fuels -- Periodicals; Petroleum -- Periodicals; 665.505
• DOI: 10.1080/10916466.2017.1421973
• Languages: English
• ISSNs: 1091-6466

Abstract

ABSTRACT: The commonly used heavy oil viscosity models are just for low water cut stage, this paper determined the influencing factors of the viscosity model in high water cut stage, by analyzing the viscosity data, presents a new and simple method base on the Process Neural Network in high water cut stage to predict the viscosity of heavy oil, which can valid measure the viscosity of heavy oil by Input parameters of the different temperature, water cut and API. Compared with the real data, the new model has the small computation error and the reliability by the process neural network new model for predicting oil viscosity. it can be tested in practices in calculating the viscosity of similar oilfields in high water cut stage.