Natural Fracture Density Prediction in Tight Sandstone Oil Reservoirs Using Multiscale Residual Fractal Responses and Heterogeneous Ensemble Learning
Xinyu Li, Yuming Liu, Guiwen Wang, Xinyu Huang, Qi Chen, Lei BaoContinuous fracture-density prediction in tight sandstone reservoirs remains challenging because image-log data are limited, conventional-log fracture responses are non-unique, and discrete fracture events are scale-mismatched with continuous well-log sequences. Taking the Chang 81 tight sandstone oil reservoir in the southwestern Ordos Basin as a case study, this study proposes a multiscale residual fractal-response constrained framework for predicting natural fracture density from conventional logs. Image-log-interpreted fracture events were converted into a P101.0m fracture-density target. Five fractal-related indicators, including the Higuchi fractal dimension, DFA/Hurst exponent, structure-function exponent, Haar local abruptness energy, and roughness index, were extracted within 2, 4, 8, and 16 m windows, residualized against ordinary statistical backgrounds, and integrated into curve-scale residual FRI, physical-group FRI, and full MA-MFRS to characterize multiscale residual fractal responses beyond conventional amplitude statistics. High-P10 intervals show curve- and scale-selective fractal responses, with CNL at 4 m and CLL8 at 2 m being the most stable positive-response combinations. Full MA-MFRS enriches high-P10 intervals mainly in the high-score tail, indicating its suitability as a fractal-response constraint rather than a standalone fracture discriminator. Based on this constraint, an MFRS-LFI Net was developed for log-response encoding, fractal-response gating, latent fracture-intensity reconstruction, and scale aggregation, and a gated heterogeneous ensemble was used to fuse multiple base learners. The final ensemble achieved R2 = 0.8929 and RMSE = 0.1435 on the two held-out test wells, G13 and Y15, whereas removing MA-MFRS reduced performance to R2 = 0.8474 and RMSE = 0.1678. Core and image-log evidence, together with 3D modeling and water-injection response comparisons, provide additional geological and dynamic consistency checks.