Abstract:

The identification of stratigraphic lithology is a key element of geological and geotechnical surveys. In this paper, a drilling measurement system is developed, and the corresponding data processing and analysis methods are proposed. Based on machine learning technology, a support vector machine (SVM) algorithm is selected to examine a scheme to invert the formation lithology by the drilling parameters. It is shown that establishing the correspondence between each parameter and borehole depth data is beneficial to the establishment of machine learning database and the implementation of the inversion scheme. The drilling measurement parameters processing principle is identifying the stop and abnormal status, and establishing drilling timehole depth and drilling parameterhole depth curves. SVM achieves good results in formation lithology identification, and the prediction results are basically consistent with drilling records, which can be used for key formation lithology identification, preventing false drilling and false compilation, and providing a new way for intelligent surveying in geotechnical engineering.

Key words:

drilling measurement system, machine learning, support vector machine, formation lithology identification