Visualization of hydraulic fracture using physics-informed clustering to process ultrasonic shear waves

Ultrasonic transmission is sensitive to the spatial variation in mechanical properties of materials due to the presence of cracks/fractures. Wave propagation through fractured media introduces changes in the frequency content, travel time and transmission coefficient of the wave. A workflow based on physics-informed unsupervised learning is developed to process the transmitted ultrasonic-shear waveforms to non-invasively visualize the geomechanical alterations due to hydraulic fracturing. Novelty of the work involves the assignment of both statistically consistent and physically consistent clusters to the measurements of shear waveforms acquired across the one axial and two frontal planes. Physically consistent/relevant information is incorporated by considering the travel time of the peak of spectral energy and transmission coefficient of the transmitted waveform. The proposed workflow generates maps of geomechanical alterations across the frontal and axial planes of the sample. The outputs of the workflow are in good agreement with independent techniques viz. acoustic emission and X-ray computed tomography. The proposed workflow can be adapted for improved fracture characterization in the subsurface when processing sonic-logging, cross-wellbore seismic or surface seismic waveform data.

Automated summary

A novel workflow based on physics-informed unsupervised learning is developed to visualize geomechanical alterations due to hydraulic fracturing through processing transmitted ultrasonic-shear waveforms. The workflow assigns both statistically consistent and physically consistent clusters to shear waveforms, taking into account factors such as travel time and transmission coefficient. The generated maps of geomechanical alterations are in good agreement with independent techniques, and the workflow can be adapted for improved fracture characterization in the subsurface.