• 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