Prediction of fault and fracture density from seismic attributes and machine learning models calibrated with borehole image logs
Author(s)
Hao, Yuxin
Bell, Rebecca
Cader, Abdulqadir
Wang, Yanghua
Type
Journal Article
Abstract
Faults and fractures play a key role in the productivity of shale gas reservoirs by affecting porosity, permeability, and gas flow paths. However, direct characterization of these small-scale features using traditional seismic methods faces significant challenges due to resolution limitations. Using a post-stack 3D seismic data set from the Jiaoshiba area in the southeastern Sichuan Basin, multiple post-stack seismic attributes and a set of convolutional neural network (CNN)-based methods from Geoteric are applied to the shale gas reservoir of Wufeng-Longmaxi Formation for deformation characterization. Wellbore fault and fracture counts were derived from Formation Micro Image (FMI) log interpretations and core sample observations, providing a reliable data set for cross-validation. This analysis involves cross-plotting seismic characterization results with borehole deformation counts to measure how these predictions correlate with wellbore data. Results indicate a near-linear correlation between borehole deformation count and seismic characterization methods, with the Combined AI network and Birch model, as well as dip illumination attribute, showing the strongest correlation. These findings suggest that seismic methods can be effective as a proxy for predicting sub-seismic scale deformation. Furthermore, pressure coefficients, porosity and gas contents have been cross-plotted with wellbore deformation counts, indicating these important factors for shale gas exploration are correlated with deformations and seismic deformation characterization methods. A prediction of porosity, pressure coefficients, and gas contents was conducted with their correlation with these seismic characterization methods. This research highlights the potential of integrated seismic and machine learning approaches in enhancing the predictability and understanding of geological deformations at scales not directly observable by direct seismic techniques and proposes an alternative method for predicting key parameters for shale gas exploration.
Date Issued
2026-05-01
Date Acceptance
2026-02-17
Citation
Basin Research, 2026, 38 (3)
ISSN
0950-091X
Publisher
Wiley
Journal / Book Title
Basin Research
Volume
38
Issue
3
Copyright Statement
© 2026 The Author(s). Basin Research published by International Association of Sedimentologists and European Association of Geoscientists and Engineers and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Publication Status
Published
Article Number
e70104
Date Publish Online
2026-05-16