Title :
Predicting Parameters of Nature Oil Reservoir Using General Regression Neural Network
Author :
Wang, Kejun ; He, Bo ; Chen, Ruolei
Author_Institution :
Harbin Eng. Univ., Harbin
Abstract :
In this paper the present statement of forecasting nonlinear systems and kinds of factors influencing the data of oil reservoir parameter were discussed. Based on these, a general regression neural network (GRNN) predicting model for oil reservoir parameters was presented. Comparing with corresponding real values, simulation results could show the effectiveness to improve the predicting accuracy and training speed by the proposed GRNN predicting models.
Keywords :
hydrocarbon reservoirs; neural nets; nonlinear systems; production engineering computing; regression analysis; general regression neural network; nature oil reservoir; nonlinear systems; parameter prediction; Accuracy; Artificial neural networks; Automation; Hidden Markov models; Hydrocarbon reservoirs; Neural networks; Nonlinear systems; Petroleum; Predictive models; Smoothing methods; GRNN; Oil reservoir parameter; Prediction; Time series;
Conference_Titel :
Mechatronics and Automation, 2007. ICMA 2007. International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-0828-3
Electronic_ISBN :
978-1-4244-0828-3
DOI :
10.1109/ICMA.2007.4303651
