DocumentCode :
2451111
Title :
The new method to predict the early productivity of ultra-low permeability reservoir in Ordos Basin
Author :
Cao, Baoge ; Chen, Mingqiang
Author_Institution :
Pet. Eng. Inst., Xi´´an Shi-you Univ., Xian, China
fYear :
2011
fDate :
24-26 June 2011
Firstpage :
2707
Lastpage :
2710
Abstract :
There are many and complex factors that affect the productivity in low-permeability reservoir well, from considering the contribution to which, total effective reservoir thickness, reservoir permeability and porosity are main factors affecting well productivity of hua-qing area in the Ordos Basin. The relationship between these factors and the productivity is a nonlinear system, so, this paper researched and derived on the BP neural network model and its algorithm, and used this model to seek the relationship between productivity and influencing factors. The results confirmed that this method is reliable to predict well productivity and can be used to predict the early well productivity of ultra-low permeability reservoirs.
Keywords :
backpropagation; hydrocarbon reservoirs; neural nets; permeability; productivity; BP neural network model; Ordos basin; reservoir porosity; ultra low permeability reservoir; well productivity prediction; Artificial neural networks; Media; Permeability; Petroleum; Productivity; Publishing; Reservoirs; BP neural network model; productivity prediction; reservoir productivity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Remote Sensing, Environment and Transportation Engineering (RSETE), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9172-8
Type :
conf
DOI :
10.1109/RSETE.2011.5964874
Filename :
5964874
Link To Document :
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