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Application of improved support vector machine in geochemical lithology identification

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Abstract

Lithology identification is an important task in oil and gas exploration. In recent years, machine learning methods have become a powerful tool for intelligent lithology identification. To address the redundancy of conventional logging data and unbalanced distribution among formation lithology classes due to the complexity of depositional environment and inhomogeneity of subsurface space, this paper investigates the affiliation-weighted one-to-one support vector machine (WOVOSVM) lithology identification method based on geochemical logging data. This method uses geochemical logging data, which can directly reflect the formation lithology information, as input, and achieves intelligent and accurate lithology classification under the calculation of WOVOSVM. In this study, Shahezi Formation of Songke 2 Well in Songliao Basin, China is taken as the experimental object, and two data sets with different distribution characteristics are selected as the input. Use WOVOSVM, Adaboost, random forest (RF) and traditional support vector machine (SVM) to identify lithology, and compare and analyze the results. The results are as follows: (1) Accuracy metrics of most of the four classification models were above 60%, indicating the geochemical logging data can effectively reflect the formation lithology information, which is a reliable indicator for the intelligent identification of logging lithology. (2) When the data set has a strong imbalance, the lithology recognition performance of WOVOSVM is better than other methods, the average value of accuracy metrics is more than 72%, F1 value is 8.77% to 14.56% higher than other models, especially in the small sample lithology category recognition, 70% of the samples are correctly classified.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This research was funded by [National Natural Science Foundation of China] grant number [41972112, 42272134]; [China Geological Survey] grant number [DD20190010].

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Authors and Affiliations

  1. China University of Geosciences (Beijing), Beijing, 100083, China

    Shitao Yin, Yongjian Huang, Zhifeng Zhang & Xiang Li

  2. Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037, China

    Shitao Yin

  3. National Research Center for Geoanalysis, Beijing, 100037, China

    Xiaochun Lin

Authors
  1. Shitao Yin
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  2. Xiaochun Lin
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  3. Yongjian Huang
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  4. Zhifeng Zhang
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  5. Xiang Li
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Contributions

All authors contributed to the study conception and design. Data collection were collected by Xiang Li and Zhifeng Zhang. The experimental models were constructed by Shitao Yin. The development and the testing of the presented methods were performed by Shitao Yin and Xiaochun Lin. The manuscript was written by Yongjian Huang and Shitao Yin. All authors attend to comment the manuscript, and all authors read and approved the final manuscript.

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Correspondence to Yongjian Huang.

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Communicated by: H. Babaie

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Yin, S., Lin, X., Huang, Y. et al. Application of improved support vector machine in geochemical lithology identification. Earth Sci Inform 16, 205–220 (2023). https://doi.org/10.1007/s12145-022-00932-2

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