Gradient boosting justification for predicting flow assurance issues in the bottomhole zone of gas-condensate wells

During the development of gas-condensate fields, companies often face flow assurance issues. They may lead to unplanned standstills of wells, damaged assets, and substantial financial losses. To help reservoir engineers to identify all the patterns of upcoming flow assurance issues and perform preventing actions timely, we propose to use a machine learning detector. In this paper, we consider the performance of a gradient boosting machine learning model while predicting two types of flow assurance issues appearing at the bottomhole zone: hydrate plugging and condensate banking. After expanding the limited set of production parameters with retrospective features and features of surrounding wells we trained the model as a binary classifier. At the blind test, such values of quality metrics were achieved as Recall ≈ 0.78 and Precision ≈ 0.64. The investigation of feature importance revealed that the changes in gas flow rate together with changes in bottomhole and wellhead pressure are the most important parameters for classification. That is fully consistent with the underlying physics.