Well workovers are an inevitable part of any oil- or gas-producing well’s lifecycle. Today, the selection of wells for performing workovers strongly relies on a set of rules evolved from the experts’ experience and the company’s standards. The rapid assessment of big data from oil and gas wells using modern machine learning (ML) models can make this process more effective, timely highlighting the decline in the efficiency of each well. One of the challenges of this task is preparing data for training. Together with such basic steps as data cleansing, extensive feature engineering and data balancing have to be performed. After that, ML models can be applied to solve a classification problem. Among all the analyzed models, gradient boosting appeared to be the most promising one. The quality metrics demonstrated that the model accurately predicts the points at which the expert planned a well workover or the possible issue that will eventually require the workover. However, together with true values, some “false alarms” appear, whose amount can be adjusted. Thus, the algorithm can be used to automate the process of analyzing vast amounts of data, prolong the lifetime of wells, and reduce operational expenses significantly.