• 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