石油与天然气地质 ›› 2007, Vol. 28 ›› Issue (3): 407-412.doi: 10.11743/ogg20070316

• 方法技术 • 上一篇    下一篇

低孔低渗储层测录井资料油气识别方法

杨思通1, 孙建孟2, 马建海3, 郇光辉4   

  1. 1. 山东科技大学, 地球信息科学与工程学院, 山东, 青岛, 266510;
    2. 中国石油大学, 地球资源与信息学院, 山东, 东营, 257061;
    3. 中国石油, 青海油田分公司, 甘肃, 敦煌, 736202;
    4. 中国石化, 胜利油田有限公司测井二公司, 山东, 东营, 257062
  • 收稿日期:2007-03-30 出版日期:2007-06-25 发布日期:2012-01-16

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)

799

被引次数

81

摘要：

青海乌南油田上油砂山组(N22)和下油砂山组(N21)储集层平均孔隙度分别为13.0%和13.6%,平均渗透分别为3.88×103μm2和2.93×103μm2,属于低孔、低渗油田。在低孔、低渗储层,由于油气储层中测井资料受储层岩性、地层水性质和储层物性等影响较大,造成含油气储层测井曲线异常特征不明显,单一应用测井资料识别油、气、水层困难。应用BP神经网络技术对乌南油田低孔、低渗储层的测井资料与录井资料进行综合处理,利用测井信息的丰富性和高分辨率的优势与录井资料识别油、气、水层的直观准确性互相结合对低孔、低渗储层进行油气识别。

关键词: 测井, 气测录井, 地化录井, BP神经网络, 低孔、低渗储层, 油气识别

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

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