A Machine Learning Framework for Predicting Fracture Dip Angle in Shale Gas Reservoirs from Pulsed Neutron Logging Responses

Abstract

Accurate characterization of fracture angles is crucial for evaluating natural gas storage and transport capacity in shale gas reservoirs. Although numerical simulations based on neutron transport theory can model well log responses at different fracture angles, the process is computationally intensive and is not suitable for field applications. This study proposes a data-driven machine learning (ML) framework to rapidly and accurately predict fracture dip angles directly from pulsed neutron logging measurements. We generated a comprehensive dataset using a high-fidelity deterministic neutron transport solver based on the discrete ordinate method. This solver provides a direct solution to the Boltzmann transport equation, simulating thermal and epithermal neutron fluxes, energy spectra, and time-dependent distributions across fracture angles ranging from 0° to 90°. Key features, including effective fracture volume (EFV), neutron count ratios, and spectral energy centroids, were extracted. We developed and compared three regression models: Random Forest (RF), Support Vector Regression (SVR), and a Deep Neural Network (DNN). The DNN model outperformed traditional approaches, achieving a coefficient of determination (R2) of 0.98 on the test dataset. Feature importance analysis using SHAP confirmed that the ratio of epithermal to thermal neutron counts and the high-energy tail (>7 MeV) of the neutron spectrum are the most discriminative features for distinguishing high-angle (>60°) from low-angle (<30°) fractures. The proposed ML framework provides a robust surrogate model for the computationally expensive neutron transport simulation, enabling real-time interpretation of fracture orientation from well logs. This approach offers a novel methodology for porosity correction and reservoir quality assessment in unconventional shale gas formations.