Title :
Apply Study on Evaluation Techniques for Oil Gas Reservoir Based on Neural Networks Techniques
Author :
He, Hong ; Jin, Shijiu ; Yang, Wenmin ; Zhu, Xianwei
Author_Institution :
State Key Lab. for Precision Meas. Technol. & Instruments, Tianjin Univ.
Abstract :
By applying principle of computer artificial nerve network´s and arithmetic of error reversion transmission, and by making parameters of gas logging quantified and standardized , it can establish the interpretation model of artificial nerve network, of which the inputting model sample is based on many parameters. Summing up various data of comprehensive logging can solve the problems of low accurate rate for identifying oil, gas and water layer. The logging nerve network is a new method, which is effective for evaluating and interpreting oil, gas and water layer synthetically. It is prove by practice, the overall coincidence rate of interpretation reaches 97%
Keywords :
geophysical prospecting; geophysics computing; neural nets; oil technology; well logging; artificial nerve network; error reversion transmission; gas layer identification; gas logging; mud logging; neural networks; oil gas reservoir; oil layer identification; water layer identification; Computer errors; Computer networks; Digital arithmetic; Electronic mail; Helium; Hydrocarbon reservoirs; Instruments; Laboratories; Neural networks; Petroleum; artificial neural networks; gas; identification; model; mud logging; oil; training; water layer;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712886
