Oil & Gas Geology ›› 2004, Vol. 25 ›› Issue (3): 338-343.doi: 10.11743/ogg20040320

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Application of neural network in well test analysis in low permeability oilfield

Wang Anhui1,3, Yu Shuying2, Zhang Yingkui3, Wang Longyuan4, Miao Deshun5, Sheng Guojun3, Liu Jiajun3   

  1. 1. China University of Geosciences, Beijing;
    2. No.1 Middle School of Ningjiang District, Songyuan, Jilin;
    3. Exploration and Development Research Institute of Jilin Oilfield Company, Songyuan, Jilin;
    4. Transportation Company of Jilin Petroleum Group;
    5. Heat and Power Plant of Jilin Petroleum Group;
    6. No.5 Oil Production Plant of Huabei Oifield Company, Renqiu, Hebei
  • Received:2004-03-15 Online:2004-06-25 Published:2012-01-16

Abstract:

"A" oilfield is a typical low permeability sandstone oil reservoir that has relatively successfully been developed in Jilin oilfield area.The well test interpretation is relatively complicated.The times of radial flow occurring on wells'pressure build-up curves account for only 20%~30% of the total times occurring in all tested wells.This paper introduces an integrated interpretation technology by integrating pattern recognition,neural network BP algorithm and well test interpretation software.It can specifically divided into the following steps:(1)analyze and interprete the bilogarithmic and semilogarithmic diagrams of wells with radial flows,and find out the pseudoslopes(m1,m2,m3 and m)in the continuous flow section,at the flex point,in the transitional section and on the straight line section of radial flow;(2)applying the neural network BP algorithm to construct the mathematical relation among m1,m2,m3 and m;(3)input the basic testing data of the wells without radial flow into the well test interpretation software,derive the m1,m2 and m3,and then derive m with BP algorithm;(4)substitute the parameters mentioned above and fitting them through to the three curves in the bilogarithmic and semilogarithmic diagrams,and in historic fitting diagram to be fitting to one another.

Key words: low permeability oilfield, well test interpretation, pattern recognition, neural network, BP algorithm

CLC Number: