DocumentCode
1596815
Title
Notice of Retraction
Well Cumulative Production Time Series Prediction Model
Author
Jiao Yuwei ; Zheng Songqing ; Zhou Xinmao ; Zhang Jing
Author_Institution
Res. Inst. of Pet. Exploration & Dev., PetroChina, Beijing, China
Volume
3
fYear
2010
Firstpage
533
Lastpage
535
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Chaotic Time Series models were constructed to predict the well cumulate liquid productions based on phase space reconstruction and Genetic Algorithm was applied to determine the parameters: the minimum embedding dimension m and time delay ??. 4 wells in Tahe Oilfield were taken as practical examples and the maximum predicted error produced by prediction models was 76.8 tons. The results show that the prediction models provide results with high accuracy, which indicates it´s promising to forecast productions based on the previous production data.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Chaotic Time Series models were constructed to predict the well cumulate liquid productions based on phase space reconstruction and Genetic Algorithm was applied to determine the parameters: the minimum embedding dimension m and time delay ??. 4 wells in Tahe Oilfield were taken as practical examples and the maximum predicted error produced by prediction models was 76.8 tons. The results show that the prediction models provide results with high accuracy, which indicates it´s promising to forecast productions based on the previous production data.
Keywords
chaos; forecasting theory; genetic algorithms; hydrocarbon reservoirs; production management; time series; Tahe oilfield; chaotic time series models; forecast productions; genetic algorithm; phase space reconstruction; reservoir management; time series prediction model; well cumulate liquid productions; Chaos; Computational modeling; Computer simulation; Delay effects; Genetic algorithms; Geology; Petroleum; Predictive models; Production; Trajectory;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Modeling and Simulation, 2010. ICCMS '10. Second International Conference on
Conference_Location
Sanya, Hainan
Print_ISBN
978-1-4244-5642-0
Type
conf
DOI
10.1109/ICCMS.2010.481
Filename
5421282
Link To Document