DocumentCode
2835258
Title
Predicting Reservoir Permeability That Improved through Explosion Fracturing by Means of Artificial Neural Network
Author
Xu, Peng ; Cheng, Yuanfang ; Wang, Guihua ; Wang, Jingyin ; Liu, Xiaolan ; Li, Lei
Author_Institution
Coll. of Pet. Eng., China Univ. of Pet., Dongying, China
fYear
2011
fDate
17-18 July 2011
Firstpage
1
Lastpage
3
Abstract
In order to predict the changes of low permeability reservoir´s permeability that improved by explosive fracturing technology effectively, we carried out some explosive fracturing tests, got cores from the samples and measured the permeability of those cores. Based on the test results and the means of artificial neural network, we built the permeability prediction model that can be used to predict the change of low permeability reservoir´s permeability that after explosive fracturing. We did prediction to some unknown samples and the result shows that the forecasted results consistent with the factual data.
Keywords
explosions; explosives; fracture; hydrocarbon reservoirs; mechanical testing; neural nets; artificial neural network; explosive fracture testing; explosive fracturing technology; permeability measurement; reservoir permeability prediction model; Artificial neural networks; Explosives; Permeability; Petroleum; Predictive models; Presses; Reservoirs;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits, Communications and System (PACCS), 2011 Third Pacific-Asia Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4577-0855-8
Type
conf
DOI
10.1109/PACCS.2011.5990088
Filename
5990088
Link To Document