A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems

Abstract

The integrity failure in gas lift wells had 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. The collected data contained 9,000 data arrays with 23 features. 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. The study models included both single and ensemble supervised learning algorithms (e.g., random forest, support vector machine, decision tree, and scalable boosting techniques). Comparative analysis of the deployed models was performed to determine the best predictive model. Further, novel evaluation metrics for the confusion matrix of each model were introduced. The results showed that extreme gradient boosting and categorical boosting outperformed all the applied algorithms. They can predict well integrity failures with an accuracy of 100% using traditional or proposed metrics. Physical equations were also developed on the basis of feature importance extracted from the random forest algorithm. The developed model will help optimize company resources and dedicate personnel efforts to high-risk wells. As a result, progressive improvements in health, safety, and environment and business performance can be achieved.

Cited as: Salem, A. M., Yakoot, M. S., Mahmoud, O. A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems. Advances in Geo-Energy Research, 2022, 6(2): 123-142. https://doi.org/10.46690/ager.2022.02.05

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