DATA-DRIVEN MODELING OF WELL PRODUCTIVITY INDEX (PI) THROUGH MACHINE LEARNING ALGORITHMS

The accurate prediction of the Well Productivity Index (PI) is critical for reservoir management, production optimization, and forecasting. Traditional methods, such as analytical correlations and decline curve analysis, are often limited by simplifying assumptions that fail to capture the complexities of heterogeneous reservoirs like those in the Niger Delta. This research addresses this limitation by developing and evaluating a data-driven framework for PI prediction using machine learning (ML) on historical production data. The study implements and compares three advanced ensemble regression algorithms—Random Forest, XGBoost, and CatBoost—to predict PI from daily records of oil, gas, and water production rates and downhole pressures. A dataset of approximately 7,000 daily records from five Niger Delta wells was utilized, with the PI target variable calculated using a proxy for reservoir pressure drawdown. A clear performance hierarchy was established among the models. Random Forest yielded the weakest performance (R² = 0.18, MAE = 65.62), while XGBoost showed substantial improvement (R² = 0.78, MAE = 34.14). CatBoost emerged as the superior model, achieving exceptional predictive accuracy with an R² of 0.95, a Mean Absolute Error (MAE) of 18.96, and a Root Mean Squared Error (RMSE) of 21.02. Residual and temporal analyses confirmed that CatBoost produced unbiased, homoscedastic errors and effectively tracked the dynamic PI trends of individual wells over time. Interpretability analyses revealed that production rates (oil, gas, and water) were the most influential predictors, a finding consistent with reservoir engineering principles. However, this also highlights a methodological caveat regarding the mathematical coupling between the model's inputs and the PI target. The study concludes that CatBoost provides a robust and highly accurate model for PI prediction from routine field data, offering a significant advantage over
traditional methods for well performance monitoring and screening in the Niger Delta context.