A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification

Author:

Zhou Kaibo1ORCID,Zhang Jianyu1ORCID,Ren Yusong1,Huang Zhen2ORCID,Zhao Luanxiao3ORCID

Affiliation:

1. Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China..

2. Wuhan Polytechnic University, School of Electrical and Electronic Engineering, Wuhan 430023, China..

3. Tongji University, School of Ocean and Earth Science, State Key Laboratory of Marine Geology, Shanghai 200092, China.(corresponding author).

Abstract

Lithology identification based on conventional well-logging data is of great importance for geologic features characterization and reservoir quality evaluation in the exploration and production development of petroleum reservoirs. However, there are some limitations in the traditional lithology identification process: (1) It is very time consuming to build a model so that it cannot realize real-time lithology identification during well drilling, (2) it must be modeled by experienced geologists, which consumes a lot of manpower and material resources, and (3) the imbalance of labeled data in well-log data may reduce the classification performance of the model. We have developed a gradient boosting decision tree (GBDT) algorithm combining synthetic minority oversampling technique (SMOTE) to realize fast and automatic lithology identification. First, the raw well-log data are normalized by maximum and minimum normalization algorithm. Then, SMOTE is adopted to balance the number of samples in each class in training process. Next, a lithology identification model is built by GBDT to fit the preprocessed training data set. Finally, the built model is verified with the testing data set. The experimental results indicate that the proposed approach improves the lithology identification performance compared with other machine-learning approaches.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Changzhou Key Laboratory of high technology

Young Elite Scientists Sponsorship Program

Publisher

Society of Exploration Geophysicists

Subject

Geochemistry and Petrology,Geophysics

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