Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements

Lawal, Ahmad; Yang, Yingjie; Baisa, Nathanael L; He, Hongmei

Abstract

This study investigates the challenges of permeability prediction in reservoir engineering, focusing on addressing uncertainties inherent in the data and modelling process, and leveraging Nuclear Magnetic Resonance (NMR) log data from the Northern Sea Volve field. The study uses a probabilistic machine learning method called Gaussian Process Regression (GPR) with different kernels, such as Matern52, Matern32, and Radial Basis Function (RBF). LSboost, K-nearest neighbour (KNN), and XGBoost are some of the existing models that are used for comparison. Performance metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination (R2) are utilized for assessment. Additionally, the uncertainty associated with different GPR kernels is analyzed, and confidence intervals are generated to provide insights into model behaviour. The inclusion of confidence intervals enhances interpretability by quantifying the range within which the true permeability value is likely to fall with a specified level of confidence, offering valuable information for decision-making processes in reservoir engineering applications. Findings demonstrate the effectiveness of GPR with Matern52 and Matern32 kernels in permeability prediction, offering competitive performance and robust uncertainty quantification. This research contributes to advancing reservoir engineering by providing a comprehensive and uncertainty-aware approach to permeability prediction.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICAAI 2024: 2024 The 8th International Conference on Advances in Artificial Intelligence
• Start Date: Oct 17, 2024
• End Date: Oct 19, 2024
• Online Publication Date: Mar 3, 2025
• Publication Date: Oct 17, 2024
• Deposit Date: Mar 20, 2025
• Publicly Available Date: Mar 20, 2025
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Pages: 54-60
• Book Title: ICAAI '24: Proceedings of the 2024 8th International Conference on Advances in Artificial Intelligence
• DOI: https://doi.org/10.1145/3704137.3704145