Early Warning of Lost Circulation Based on Physical Models and a Hybrid Neural Network

Lost Circulation (LC) is one of the most common and high-risk complex situations encountered during drilling operations, posing a serious threat to the safe extraction and economic viability of oil and gas resources. Traditional wellbore leakage detection methods based on human experience often suffer from delays and uncertainties, making it difficult to meet real-time warning requirements under complex geological conditions. This paper proposes an LC warning method that combines a physical model with a combination of neural networks (Crested Porcupine Optimizer (CPO)–Long Short-Term Memory (LSTM)–Random Forest (RF)). The physical model utilises changes in mud pit volume, inlet–outlet flow rate differences, and riser pressure to construct interpretable event labels, thereby enhancing the physical plausibility of prediction results. The deep learning component employs LSTM networks to extract temporal features and RF for non-linear discrimination and introduces the CPO algorithm for feature selection and hyperparameter optimisation, thereby enhancing the model’s stability and generalisation capability. Validation using actual field data from the western Bohai Bay oilfield demonstrates that the proposed method outperforms traditional models in accuracy, precision, recall, and F1-score. It also offers a significant improvement in early warning time, detecting potential leakage about 17 min before traditional methods. These results highlight the effectiveness of the approach in managing risks during drilling operations.