A Machine Learning Method for the Risk Prediction of Casing Damage and Its Application in Waterflooding

Abstract

During the development of oilfields, casings in long-term service tend to be damaged to different degrees, leading to poor development of the oilfields, ineffective water circulation, and wasted water resources. In this paper, we propose a data-based method for predicting casing failure risk at both well and well-layer granularity and illustrate the application of the method to GX Block in an eastern oilfield of China. We first quantify the main control factors of casing damage by adopting the F-test and mutual information, such as that of the completion days, oil rate, and wall thickness. We then select the top 30 factors to construct the probability prediction model separately using seven algorithms, namely the decision tree, random forest, AdaBoost, gradient boosting decision tree, XGBoost, LightGBM, and backpropagation neural network algorithms. In terms of five evaluation indicators, namely the accuracy, precision, recall, F1-score, and area under the curve, we find that the LightGBM algorithm yields the best results at both granularities. The accuracy of the prediction model based on the preferred algorithm reaches 87.29% and 92.45% at well and well-layer granularity, respectively.