A Machine Learning Model for Real-Time Early Kick and Loss Prediction EKLP Application

Abstract Early Kick and Loss Prediction (EKLP) is crucial in drilling operations to maintain well control and prevent catastrophic events such as blowouts. Real-time detection allows for immediate corrective actions, minimizing risks and costs. This paper presents a CNN-LSTM hybrid model, leveraging Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for time series analysis. The model processes real-time data from various drilling metrics, including Rate of Penetration (ROP), Weight on Bit (WOB), Revolutions Per Minute (RPM), Standpipe Pressure (SPP), flow values, pump rates, and mud pit volumes. Trained on a dataset of normal operations and confirmed kick/loss events, the CNN-LSTM model demonstrated high accuracy and speed in predicting abnormal events, significantly outperforming traditional detection methods. The model was integrated into a real-time monitoring system for practical applications, showing superior ability to process sensor data and predict kick and loss events. This hybrid approach enhances operational efficiency and decision-making in drilling. In conclusion, the CNN-LSTM model provides a robust and reliable solution for real-time EKLP, combining the strengths of CNN and LSTM to handle complex drilling data. This approach improves detection accuracy and system adaptability, making it a vital tool for modern drilling operations. The model's potential to address other drilling challenges, such as stuck pipe events and drill bit fouling, further highlights its versatility and utility.