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Chan Plot Signature Identification as a Practical Machine Learning Classification Problem


 Creating Chan water control diagnostic plots is a common well surveillance activity to search for signatures that distinguish and explain mechanisms behind excessive water production in oil wells. The technique involves an engineer who visually classifies patterns or signatures related to a water production mechanism. This study shows how the Chan plot signature identification can be approached as a machine learning (ML) classification problem, where a well can be characterized by the slopes of water-oil ratio (WOR) and WOR time derivative (WOR’) curves. A model tries to find the pattern category to which that well belongs. Having ML models that can predict whether a well belongs to a specific Chan plot signature, or pattern, would be valuable as a well surveillance tool, especially in high-well-count fields.
 Our previous work focused on using the shape of the Chan plot as features for a radial basis function (RBF) support vector machines (SVM) model. In this study, we examine how features to identify Chan plot signatures can be simplified and how different ML models compare in accuracy. ML models used in this study were: nearest neighbor, SVM, decision tree, random forest, logistic regression, and Naive Bayes. In this study, we use the slopes of WOR and WOR’ as features. As a result, we observed an increase in the accuracy of the ML models that we used. By performing the quality check on the data set after selecting slopes as features, we identified that the dataset contained several incorrectly labeled examples, which we adjusted before we trained the ML models. By comparing the models’ metrics in the context of the test set, we identified that the ML model with the highest f1-score was nearest neighbor at 0.93, whereas the RBF SVM model achieved a value of 0.90. We also compared models’ decision boundaries to find how they differ among all ML models.
 We obtained an improved accuracy of an ML model by simplifying features as well as raising the quality of data used in the Chan plot signature identification problem. These ML models could be useful in automatic classification whether a well exhibits a specific Chan plot signature, to flag it for a review within a broader petroleum engineering decision framework.