The prediction of production decline rate in the development of offshore high water-cut reservoirs predominantly relies on the traditional Arps decline curves. However, the solution process is complex, and the interpretation efficiency is low, making it difficult to meet the demand for rapid prediction of production decline rates. To address this, this paper first identifies the key influencing factors of production decline rate through comprehensive feature engineering. Subsequently, it proposes a novel prediction method for the production decline rate in offshore high water-cut reservoirs by integrating Moth–Flame Optimization with Extreme Gradient Boosting Tree (MFO-XGBoost). This method utilizes seven dynamic and static influencing factors, namely vertical thickness, perforated thickness, shale content, permeability, crude oil viscosity, formation flow coefficient, and well deviation angle, to predict the production decline rate. The forecasting outcomes of the MFO-XGBoost method are then compared with those of standard RF, standard DT, the standalone XGBoost model, and the calculated results from the exponential decline model. Additionally, the forecasting capability of the MFO-XGBoost method is benchmarked against Particle Swarm Optimization–XGBoost (PSO-XGBoost) and Bayesian Optimization–XGBoost methods for predicting the production decline rate in offshore high water-cut reservoirs. The findings from the experiments show that the MFO-XGBoost method can achieve accurate prediction of the production decline rate in offshore high water-cut reservoirs, with a coefficient of determination (R²) reaching 0.9128, thereby providing a basis for strategies to mitigate the production decline rate.

To address the challenges of large-scale production data, complex temporal dynamics, and the difficulty in extracting key reservoir performance indicators, this study proposes an intelligent time-series analytics approach, validated using an offshore oilfield case. The methodology integrates a cascaded outlier detection framework combining the 3-Sigma rule and the One-Class Support Vector Machine (OC-SVM). The 3-Sigma rule is first used for rapid statistical screening of extreme outliers, followed by OC-SVM for nonlinear anomaly detection, enhancing the accuracy of dynamic production data preprocessing. Key indicators—including initial production capacity, decline rate, water-cut trend, and recoverable reserves—are automatically extracted through hybrid modeling combining production decline analysis and waterflood characteristic curves. Algorithm reliability is rigorously evaluated using error metrics (SSE: Sum of Squared Errors, MSE: Mean Squared Error, MAE: Mean Absolute Error, RMSE: Root Mean Squared Error) and goodness-of-fit (R²). Experimental results demonstrate that the proposed method outperforms manual extraction, achieving <10% error in daily oil production and waterflood performance curve fitting, while significantly enhancing accuracy and automation. This framework provides a robust data−driven foundation for intelligent reservoir management.