Application of improved support vector machine in geochemical lithology identification

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.