Comparing SVM and DBSCAN for Outlier Detection in Oil Well Monitoring Data

The aim of this study is to compare One-Class Support Vector Machine (One-Class SVM) and DBSCAN for detecting statistical anomalies in oil-well monitoring data and to assess their joint use. Six parameters from a multiphase water-cut meter were analyzed; Principal Component Analysis (PCA) reduced them to three principal components preserving 89.86% of the total variance. In a sample of 8639 observations, One-Class SVM identified 424 anomalous points and DBSCAN identified 188, with 85 observations detected by both methods. For flow-regime classification by gas-content level, SVM with an RBF kernel (C = 100.0, gamma = 1.0) achieved 98.26% accuracy with an F1 score of 0.97-0.99 on a stratified 70/30 split. Five-fold stratified cross-validation yielded an F1_macro of 0.9853±0.0026. Joint interpretation separates points detected by both methods from SVM-only and DBSCAN-only detections for subsequent expert review.

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