الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The integrity failure in gas lift wells has been proven to be more severe than other artificial lift wells across the industry. Accurate risk assessment is an essential requirement for predicting well integrity failures. In this study, a machine learning model was established for automated and precise prediction of integrity failures in gas lift wells. Data arrays were structured and fed into 11 different machine learning algorithms to build an automated systematic tool for calculating the imposed risk of any well. Novel evaluation metrics for the confusion matrix of each model were introduced. The results showed that extreme gradient boosting (XGB) and categorical boosting (CatBoost) outperformed all applied algorithms. They can predict well integrity failures with an accuracy of 100% using traditional or proposed metrics. A physical equation was also developed.