Summary This research addresses one of the most critical challenges in oilwell drilling: the early detection of kick anomalies. A kick anomaly is characterized by an undesirable influx of formation fluids into the wellbore, which can lead to severe safety hazards, making its timely identification essential to ensure the safety of all personnel involved and to maintain operational continuity. As kick cases are rare and operational methods often overestimate their occurrence, deep learning anomaly detection techniques are valuable for enhancing efficiency and minimizing false alarms. By using different autoencoder (AE) models, especially with convolutional networks, the aim of this study is not only to improve the accuracy of kick detection but also to anticipate the detection significantly. Based on the analyses of kick anomalies in deepwater offshore wells located in pre-salt Brazilian oil fields, we used real-time drilling data from correlation wells without anomalies to train different AE architectures. The data set comprised 730 hours of historical real-time monitoring records from eight offshore drilling operations across two different oil fields, during which two real kicks were documented. The results indicate that the 1D convolutional neural network AE (1D CNN-AE) outperforms five other well-established AE models reported in the literature for detecting kicks during offshore drilling operations. The 1D CNN-AE model demonstrated high efficacy in the early identification of kick events, achieving a recall rate of 100%. Furthermore, it yielded a 31% reduction in the historical false kick rate (FKR), indicating a substantial improvement in event classification accuracy. In addition to its predictive reliability, the model provided a mean early detection time (EDT) of approximately 100 minutes prior to the corresponding operational reports, reinforcing its potential as a robust tool for real-time decision support in well control operations. This study presents three original contributions: (i) a framework outlining the neural network techniques used in the literature to address the kick detection problem; (ii) a comprehensive investigation and preprocessing of kick event data recorded between 2018 and the first semester of 2025 in specific deepwater offshore wells in Brazil; and (iii) a prototype based on a 1D CNN-AE architecture.
Lima et al. (Sun,) studied this question.