Research on intelligent kick early warning technology based on LSTM-AE unsupervised learning

In oil and gas exploration, kick is a typical high-risk downhole incident. Currently, most intelligent kick detection methods belong to supervised learning, which depends on labeled samples and is prone to class imbalance issues in the training set, leading to reduced model accuracy and higher false alarm rates in practical applications. To address this, this paper focuses on the application of unsupervised learning in kick detection, proposing a kick detection method based on Long Short-Term Memory Autoencoder (LSTM-AE) combined with parameter trend change rules. The study integrates LSTM-AE with expert knowledge to develop an intelligent kick early warning system suitable for well sites, validated using field data from five wells. The results show that the average reconstruction mean squared error of the LSTM-AE is 0.017, with an average false alarm rate of 4.56% for kick detection, detecting kicks an average of 9.2 minutes earlier than manual detection. This outcome confirms that kick detection can utilize unsupervised learning methods, avoiding relying on labeled samples and class imbalance issues in the training set, thereby effectively improving model accuracy and generalization ability. Moreover, the proposed LSTM-AE demonstrates superior performance in kick detection, and the detection method combining the model and judgment rules has significant implications for monitoring and early warning of other types of downhole incidents.