Real-Time Prediction of Bottom Hole Pressure via Graph Neural Network

Accurately and efficiently predicting bottomhole pressure (BHP) is of great importance for safe drilling in complex formations. Many researchers have conducted extensive investigations into intelligent BHP prediction techniques. However, the current intelligent models mostly focus on the data-driven relationship between logging parameters and BHP, and less on the influence of the correlation between the logging parameters on the BHP. This paper proposes a real-time prediction framework based on graph neural networks. Our model selects input features based on drilling mechanisms and statistical analyses, and utilizes adaptive learning of the graph based on multivariate time-series parameters to capture the relationship between multivariate logging parameters and BHP. Finally, the model performance is thoroughly analyzed based on field drilling datasets after optimizing model hyperparameters using the Bayesian optimization method. Results indicate that the proposed method performs better in terms of prediction accuracy, captures the inflection points of curve changes better, and is more robust under the new well section. The mean absolute percentage error of the method reaches 1.28% which is reduced by 25% compared with other traditional intelligent models. This study provides a solution for achieving accurate real-time predictions of bottom hole pressure, establishing a solid foundation for the realization of precise pressure control during drilling operations.