Evaluation of Influential Parameters for Prediction of Flowing Bottom Hole Pressure in Dry Gas Wells Using Machine Learning
This study evaluates the prediction of flowing bottom-hole pressure (FBHP) in dry gas wells using machine learning techniques, specifically Random Forest and Artificial Neural Network (ANN) models. Unlike earlier work based on PROSPER-generated synthetic data, this study utilizes a real field dataset of 206 samples obtained from the ProBHP repository, originally compiled by Govier and Fogarasi (1975) and Asheim (1986). The dataset comprises 10 input variables, including production rates, well depth, tubing size, temperatures, and wellhead pressure, with measured bottom-hole pressure (MBHP) as the target. Feature importance analysis identified well depth, oil rate, water rate, and wellhead pressure as the most influential parameters. The data were split into 80% training and 20% testing sets, with Z-score-based outlier removal reducing the training data slightly. The Random Forest model showed strong predictive performance, achieving a test R2 of 0.81, MAE of 93.73 psig, and RMSE of 123.39 psig, with a cross-validation R2 of 0.72 ± 0.11. In contrast, the ANN model performed poorly, with a test R2 of 0.05 and MAE of 209.55 psig. Overall, the results highlight the reliability of Random Forest for FBHP prediction using real field data, while also showing the limitations of a simple ANN model on small, noisy datasets. The identified key parameters provide useful insights for well performance monitoring and production optimization in dry gas systems.