%0 Dataset
%T Research dataset on automatic formation lithology identification based on integrated logging data (2009-2013)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/ec7c79a4-b7b1-4926-916d-6f130c096a16
%W NCDC
%R 10.12072/ncdc.xda14.db6396.2024
%A ChenDong
%K Devonian Donghetang Formation;logging curve;oil and gas logging;Tarim Basin;lithology identification
%X The lithology recognition method is based on a machine learning classification algorithm, which can classify and recognize logging data and logging diagrams, and effectively learn and memorize the characteristics of the rock layers in the reservoir. The method can compare four algorithms, namely adsorbed rock body, decision tree, random forest and SVM, to select the highest lithology recognition accuracy. It provides an important basis for meeting the requirements of recognition accuracy and efficiency, and is of great significance for automatic interpretation of logging data and formation self-recognition by computer. The data content is as follows: ILD：Induction Logging Device; SP：Spontaneous Potential; CN：Neutron Logging; ILM：Intermediate Logging Device; GR：Gamma Ray Logging; CAL：Caliper; AC：Acoustic Logging; DEN：Density Logging.