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.
The original logging data are: well logging data of the Devonian Donghetang Formation in the Tarim Basin.
Integration of logging data and automatic lithology identification using machine learning methods.
Data quality is good.
| # | number | name | type |
| 1 | XDA14000000 | Strategy Priority Research Program (Category A) of Chinese Academy of Sciences | |
| 2 | XDA14040000 | Key technologies for ultra-deep guided drilling | Strategy Priority Research Program (Category A) of Chinese Academy of Sciences |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.XDA14.DB6396.2024
10.12072/ncdc.xda14.db6396.2024
