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