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http://dx.doi.org/10.7842/kigas.2018.22.6.90

Development of Productivity Prediction Model according to Choke Size and Gas Injection Rate by using ANN(Artificial Neural Network) at Oil Producer  

Han, Dong-kwon (Dept. of Energy and Mineral Resources Engineering, Dong-A University)
Kwon, Sun-il (Dept. of Energy and Mineral Resources Engineering, Dong-A University)
Publication Information
Journal of the Korean Institute of Gas / v.22, no.6, 2018 , pp. 90-103 More about this Journal
Abstract
This paper presents the development of two ANN models which can predict an optimum production rate by controlling choke size in oil well, and gas injection rate in gas-lift well. The input data was solution gas-oil ratio, water cut, reservoir pressure, and choke size or gas injection rate. The output data was wellhead pressure and production rate. Firstly, a range of each parameters was decided by conducting sensitive analysis of input data for onshore oil well. In addition, 1,715 sets training data for choke size decision model and 1,225 sets for gas injection rate decision model were generated by nodal analysis. From the results of comparing between the nodal analysis and the ANN on the same reservoir system showed that the correlation factors were very high(>0.99). Mean absolute error of wellhead pressure and oil production rate was 0.55%, 1.05% with the choke size model, respectively. And the gas injection rate model showed the errors of 1.23%, 2.67%. It was found that the developed models had been highly accurate.
Keywords
choke size; gas lift; artificial neural network; nodal analysis; gas injection rate; oil production rate;
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