Duan Yajun, Xie Jun, Su Yanchun, Liang Huizhen, Hu Xiao, Wang Qizhen, Pan Zhiping
College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China.
CNOOC China Limited, Tianjin Branch, Tianjin, 300459, China.
Sci Rep. 2020 Nov 5;10(1):19209. doi: 10.1038/s41598-020-76303-y.
The decision tree method can be used to identify complex volcanic rock lithology by dividing lithology sample data layer by layer and establishing a tree structure classification model. Mesozoic volcanic strata are widely developed in the Bohai Bay Basin, the rock types are complex and diverse, and the logging response is irregular. Taking the D oilfield of the Laizhouwan Sag in the Bohai Bay Basin as an example, this study selects volcanic rocks with good development scales and single-layer thicknesses of more than 0.2 m as samples. Based on a comparison of various lithology identification methods and both coring and logging data, using the decision tree analysis method and the probability density characteristics of logging parameters, six logging parameters with good sensitivity to the response of the volcanic rocks of the above formation are selected (resistivity (RD), spontaneous potential (SP), density (ZDEN), natural gamma ray (GR), acoustic (DT), and compensated neutron correction (CNCF) curves), which are combined to form a lithology classifier with a tree structure similar to a flow chart. This method can clearly express the process and result of identifying volcanic rock lithology with each logging curve. Additionally, crossplots and imaging logging are used to identify the volcanic rock structure, and the core data are used to correct the identified lithology. A combination of conventional logging, imaging logging and the decision tree method is proposed to identify volcanic rock lithology, which substantially improves the accuracy of rock identification.
决策树方法可通过对岩性样本数据进行逐层划分并建立树形结构分类模型来识别复杂火山岩岩性。渤海湾盆地中生代火山地层广泛发育,岩石类型复杂多样,测井响应不规则。以渤海湾盆地莱州湾凹陷D油田为例,本研究选取发育规模良好且单层厚度大于0.2米的火山岩作为样本。基于对各种岩性识别方法以及取心和测井数据的比较,利用决策树分析方法和测井参数的概率密度特征,选取对上述地层火山岩响应具有良好敏感性的六个测井参数(电阻率(RD)、自然电位(SP)、密度(ZDEN)、自然伽马射线(GR)、声波(DT)和补偿中子校正(CNCF)曲线),将它们组合形成一个具有类似于流程图树形结构的岩性分类器。该方法能够清晰地表达利用各条测井曲线识别火山岩岩性的过程和结果。此外,利用交会图和成像测井识别火山岩结构,并利用岩心数据对识别出的岩性进行校正。提出了常规测井、成像测井与决策树方法相结合的方式来识别火山岩岩性,大大提高了岩石识别的准确性。