Enhancing pore pressure prediction accuracy: A knowledge-driven approach with temporal fusion transformer

Abstract

Pore pressure plays a crucial role in various fields, including geology, petroleum engineering, hydrology, and carbon capture, utilization, and storage (CCUS). Traditional pore pressure prediction methods, which rely on physical models, often fail to meet the required accuracy standards due to their inflexibility and technical parameterization. Although machine learning methods have been increasingly used for pore pressure prediction, they struggle to integrate domain-specific knowledge and capture the inherent temporal dynamics of the data, resulting in suboptimal prediction accuracy. To address these limitations, this study introduces the Knowledge-Aware TFT model, which determines the key parameter attributes for pore pressure prediction by comprehensively analyzing well logging data, rock mechanics theory, and pore pressure evolution theory. Additionally, the model effectively captures the temporal dynamic characteristics of the data, thereby enhancing the accuracy of pore pressure prediction. To validate the model's accuracy, uncertainty theory is employed to evaluate the experimental results, providing a description of the uncertainty associated with pore pressure prediction and supporting credibility assessment. A case study involving three vertical wells in Block X, China, compares the Knowledge-Aware TFT model with SVM, XGBoost, MLP, and LSTM models, further confirming its superior prediction accuracy (R^2 = 0.997, RMSE = 0.135) and demonstrating its practicality in pore pressure prediction. In conclusion, the proposed pore pressure prediction model not only improves prediction accuracy but also achieves excellent results in practical applications, making it a viable alternative to traditional technologies.