Title :
Evaluation Techniques for Oil Gas Reservoir Based on Artificial Neural Networks Techniques
Author :
Pan, Hong Yan ; He, Hong
Author_Institution :
Dept. of Comput. Sci., Tianjin Broadcast & TV Univ., Tianjin, China
Abstract :
By using BP artificial nerve network´s error reversion transmission. Summing up various data of comprehensive logging can solve the problem of low accurate rate for identifying oil, gas, water zones. The software provides nerve network reservoir interpretation model by studying and training the initial data of tested oil. Practice proves the overall coincidence rate of interpretation reaches 97%. It can more efficiently reflects logging technique´s advantage of wellsite quick evaluation oil, gas, water zones. The application in this technique improves the level of logging data interpretation and evaluation.
Keywords :
backpropagation; hydrocarbon reservoirs; neural nets; petroleum industry; BP artificial nerve network; artificial neural network technique; error reversion transmission; evaluation technique; logging data interpretation; network nerve reservoir interpretation; oil gas reservoir; tested oil; water zone; Artificial neural networks; Biological neural networks; Data models; Hydrocarbon reservoirs; Reservoirs; Training; Gas and Water Layer; Identification; Mud Logging; Oil; artificial neural Networks;
Conference_Titel :
Business Intelligence and Financial Engineering (BIFE), 2010 Third International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7575-9
DOI :
10.1109/BIFE.2010.17
