Lithology Logging Recognition Technology Based on GWO-SVM Algorithm

Shengyan Lu, Moujie Li, Na Luo, Wei He, Xiaojun He, Changjian Gan, Rui Deng and Javier Martinez Torres

Mathematical Problems in Engineering, 2022, vol. 2022, 1-11

Abstract: Accurate identification of lithology is the basis and key process of fine logging interpretation and evaluation. However, reservoirs formed by different sedimentary environments and tectonic movements generally have the characteristics of complex and diverse lithology and strong heterogeneity, which brings great difficulty to the identification of reservoir lithology. This paper proposes an automatic identification technology for lithology logging based on the GWO-SVM algorithm model. The technology is actually applied, and the results are compared with the results of the support vector machine cross-validation optimization model, PNN (probabilistic neural network) model, and ELM (extreme learning machine) model. The results show that the GWO-SVM lithology logging recognition model can efficiently solve the lithology recognition and classification problems in complex reservoir analysis and has strong adaptability and higher recognition accuracy.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1640096

DOI: 10.1155/2022/1640096

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