Journal of Jilin University(Earth Science Edition) ›› 2015, Vol. 45 ›› Issue (2): 649-654.doi: 10.13278/j.cnki.jjuese.201502306

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Research on Gas Yield and Level Prediction for Post-Frac Tight Sandstone Reservoirs

Pan Baozhi1, Shi Yujiang2, Jiang Bici1, Liu Dan1, Zhang Haitao2, Guo Yuhang1, Yang Xiaoming2   

  1. 1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China;
    2. Research Institute of Exploration and Development, Changqing Oilfield Compary PetroChina, Xi'an 710018, China
  • Received:2014-04-18 Published:2015-03-26

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Abstract:

Tight sandstone reservoirs are always with the characterized by the low porosity, low permeability, and low gas saturation, hardly have any natural capacity,needing fracturing for productivity, fracturing capacity prediction is necessary. We mainly research the method of the gas productivity and level prediction for post-frac tight sandstone reservoirs in the eastern of Sulige region of the Ordos basin. The prediction model is established by neural networks with the logging parameters (RT, GR, AC,CNL,DEN)and fracturing parameters(the amount of sard per meter,the sand ratio,the sand concentration,delivery capacity per meter,total amount of fluid injection the well per meter). The neural networks are Elman neural network, support vector machine (SVR) neural network, and GRNN neural network. We compared the results of three models, and picked up the best model GRNN model to predict the level of the gas productivity.

Key words: Elman networks, support vector regression, GRNN networks, Sulige area, tight sandstone, fracturing capacity

CLC Number: 

  • P631.8

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