Abstract:

Lithology identification is an important task in reservoir evaluation. With the development of machine learning methods, intelligent lithology identification has become a popular research direction. Logging while drilling (LWD) technology has been widely used. However, in the actual production process, due to the high-temperature and high-pressure operating conditions, only a few logging parameters can be measured by LWD. Due to the small number of logging parameters,  machine learning model is not able to fully tap into the few parameters. To solve this problem, this paper introduced random tree embedding into LWD lithology identification. The low dimensional LWD data was encoded by the binary tree and transformed into high dimentional sparse features, and  the upgraded data was used for training to improve the discriminative ability of the machine learning model. The comparative experiment results in this paper show that the random forest method with random tree embedding has the best recognition effect, the accuracy and F1 value are improved by 3.16% and 3.25% respectively, compared with the direct use of random forest, and outperforms the gradient boosted tree, extremely random tree and particle swarm optimization support vector machine algorithms.

Key words: machine learning, random tree embedding, random forest, lithology identification, logging while drilling