Oil & Gas Geology ›› 2007, Vol. 28 ›› Issue (3): 407-412.doi: 10.11743/ogg20070316

Previous Articles     Next Articles

Identification of hydrocarbons in low-porosity and low-permeability reservoirs by integration of surface log data with wire log information

Yang Sitong1, Sun Jianmeng2, Ma Jianhai3, Huan Guanghui4   

  1. 1. Geoinformation Science & Engineering College, Shandong University of Science and Technology, Qingdao, Shandong 266510;
    2. School of Earth Resources and Information, China University of Petroleum, Dongying, Shandong 257061;
    3. Qinghai Oilfield Company, PetroChina, Dunhuang, Gansu, 736202;
    4. No.2 Well Logging Company ofShengli Oilfield, SINOPEC, Dongying, Shandong 257062
  • Received:2007-03-30 Online:2007-06-25 Published:2012-01-16

PDF (PC)

840

Cited

14

Abstract:

Reservoirs in the Upper Youshashan Formation(N22)and the Lower Youshashan Formation(N21) of Wunan oilfield in Qinghai Province are low in porosity(with average porosity at 13.0% and 13.6% respectively) and permeability(with average permeability at 3.88?103 μm2 and 2.93?103 μm2).In this type of reservoirs,logging data is greatly affected by the reservoir lithoglogy,formation water characteristics,and reservoir physical properties,causing difficulties in distinguishing the curves of oil/gas-bearing layers and differentiating oil and gas layers from water layers according to logging data only.In this paper,BP neural network technology is applied to the integrated processing of wire logging data and surface logging data from the low-porosity and low-permeability reservoirs in Wunan oilfield.The abundance and thigh resolution of wire log information are combined with the directness and accuracy of surface log data to identify hydrocarbon zones in reservoirs with low porosity and low permeability.

Key words: wire log, gas log, geochemical log, BP neural network, low-porosity and low-permeability reservoir, hydrocarbon identification

CLC Number: