Hydrate logging curve stratification method in Muli area
XU Wei1, 2, LIN Zhen-Zhou2, 3, PAN He-Ping2, QIN Zhen2, DENG Cheng-Xiang2, QIN Rui-Dong2, JI Yang2
1.Shandong Zhengyuan Construction Engineering Co.Ltd,Ji'nan 250101,China; 2. Institute of Geophysics and Geomatics,China University of Geosciences (Wuhan),Wuhan 430074,China; 3. Institute of Geophysical and Geochemical Exploration,CAGS,Langfang 065000,China
Abstract:The Qilian Mountain is the first area of finding natural gas hydrate in the middle latitudes of the earth. The logging curve stratification is the first important work of logging interpretation, which is of great significance for the evaluation of gas hydrate logging. So far, natural gas hydrate has been found in many wells of scientific experimental drilling in the Muli area. Based on the logging data of the hydrate drilling wells in the Muli area, the authors used the activity method, the extreme variance method and the Walsh transformation method for automatic stratification. According to the basic principle of automatic stratification, different parameters were used for automatic stratification. A comparative study shows that the stratification effect of the activity method is the best, the stratified effect of the extreme variance method is poor, and the Walsh transform method has the worst stratification effect. The stratification results of the activity method are in good agreement with the lithologic data, thus laying the foundation for the lithologic identification and logging evaluation.
[1] 朱红涛, 黄众, 刘浩冉, 等. 利用测井资料识别层序地层单元技术与方法进展及趋势[J]. 地质科技情报, 2011, 30(4): 29-36. [2] 肖波, 韩学辉, 周开金, 等. 测井曲线自动分层方法回顾与展望[J]. 地球物理学进展, 2010 (5): 1802-1810. [3] Li X, Liu D, Luo J. The auto shape recognize method of well logging curve[C]//Information Science and Engineering (ICISE), 2010 2nd International Conference on. IEEE, 2010:4129-4132. [4] 张明玉. 极值方差聚类法在测井分层取值中的应用——以莫北油田莫井区为例[J]. 新疆石油地质, 2002, 23(5): 429-431. [5] 鲍晓欢. 测井曲线的最优分割法自动分层评价[J]. 海洋石油, 2005, 25(1): 81-84. [6] 易觉非. 利用活度分层法实现测井自动地质分层[J]. 石油天然气学报, 2007, 29(1): 78-80. [7] Maiti S, Tiwari R K. Automatic detection of lithologic boundaries using the Walsh transform: A case study from the KTB borehole[J]. Computers & geosciences, 2005, 31(8): 949-955. [8] 赵军. 基于数域上的模糊模式识别在测井曲线分层中的应用[J]. 测井技术, 1998, 22(4): 264-266. [9] 黄布宙, 潘保芝, 李舟波. 改进的模糊模式识别方法在测井曲线分层中的应用[J]. 物探化探计算技术, 2002, 24(2): 119-123. [10] Silva A A, Neto I A L, Misságia R M, et al. Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information[J]. Journal of Applied Geophysics, 2015, 117: 118-125. [11] Pan S Y, Hsieh B Z, Lu M T, et al. Identification of stratigraphic formation interfaces using wavelet and Fourier transforms[J]. Computers & Geosciences, 2008, 34(1): 77-92. [12] Xin-hu L. Application of wavelet analysis to well logging sequence stratigraphic division[J]. Advances in Biomedical Engineering, 2012, 8: 203. [13] Mukherjee B, Srivardhan V, Roy P N S. Identification of formation interfaces by using wavelet and Fourier transforms[J]. Journal of Applied Geophysics, 2016, 128: 140-149. [14] 黄维婷. 多尺度小波分析及其在测井曲线自动分层中的应用研究[D]. 成都:成都理工大学, 2012.