Forecasting Oil Production of Well 159-F-14H in the Volve Field Using Machine Learning Model - Rhamadhani, Devy Ayu; Saputri, Eriska Eklezia Dwi; Sari, Riska Laksmita (Indonesian Journal of Artificial Intelligence and Data Mining, 2024)
Petroleum engineers require information about the production performance of a well in order to know when the well is no longer feasible to produce. By using the approachment technique of machine learning, the research was conducted using a tree-based regression model, Random Forest Regressor, Extra Trees Regressor, and Gradient Boosting Regressor. This research was done by predicting the production of an existing well in the Volve field, namely well 159-F-14H using its field data; average downhole pressure, average downhole temperature, average wellhead temperature, average wellhead pressure, on-stream hours, average choke size percentage, gas volume from well, water volume from well. The data used is 1093 days and 70% is used for training and as much as 30% for testing. A comparative study was carried out on the predictive performance of the three models. Random Forest shows the best testing result as well as RMSE 5.134 and R2 0.974, followed by Gradient Boosting shows RMSE 5.927 and R2 0.965, and Extra Trees shows RMSE 6.524 and R2 0,958.