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
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;
Conference_Titel :
Circuits, Communications and System (PACCS), 2011 Third Pacific-Asia Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4577-0855-8
DOI :
10.1109/PACCS.2011.5990088