An early warning system for oil wells based on improved long short-term memory network

Timely and accurate oil well production warnings are crucial for optimizing oilfield management and enhancing economic returns. Traditional methods for predicting oil well production and early warning systems face significant limitations in terms of adaptability and accuracy. Artificial intelligence offers an effective solution to address these challenges. This study focuses on the ultra-high water cut stage in water-driven medium-to-high permeability reservoirs, where the water cut-defined as the ratio of produced water to total produced fluid-exceeds 90%. At this stage, even small fluctuations in water cut can have a significant impact on oil production, making it a critical early warning indicator. We use statistical methods to classify wells and define adaptive warning thresholds based on their unique characteristics. To further improve prediction accuracy, we introduce a Long Short-Term Memory (LSTM) model that integrates both dynamic and static well features, overcoming the limitations of traditional approaches. Field applications validate the effectiveness of the model, demonstrating reduced false alarms and missed warnings, while accurately predicting abnormal increases in water cut. The early warning system helps guide the adjustment of injection and production strategies, preventing water cut surges and improving overall well performance. Additionally, the incorporation of fracture parameters makes the model suitable for reservoirs with fractures.