Research on Drilling Rate of Penetration Prediction Method Based on Decision Tree Algorithm

Abstract Petroleum exploration and development confront complex geological and engineering conditions, where the selection of key parameters and tools directly impacts drilling efficiency and per-barrel development costs. Currently, drilling parameter decisions are primarily based on prior knowledge and offset well data, which introduces significant subjectivity and fails to deliver optimal solutions. This study develops a gradient boosting decision tree (GBDT) model based on decision tree algorithms for rate of penetration (ROP) prediction, utilizing drilling data from a specific Bohai Sea block for model training and validation. The model demonstrates capability in handling complex scenarios including high-dimensional sparse features and nonlinear relationships, while exhibiting robustness to missing data features. To validate the model’s effectiveness, comparative experiments were conducted with linear regression and back propagation (BP) neural network models. The results indicate that the decision tree-based predictive model achieves higher accuracy, providing valuable references for enhancing operational efficiency and reducing drilling costs.