Inferring fracture dilation and shear slip from surface deformation utilising trained surrogate models

Salimzadeh, Saeed; Kasperczyk, Dane; Sayyafzadeh, Mohammad; Kadeethum, Teeratorn

doi:10.1016/j.ijrmms.2025.106077

Title: Inferring fracture dilation and shear slip from surface deformation utilising trained surrogate models

Journal Article · 28 February 2025 · International Journal of Rock Mechanics and Mining Sciences

DOI: https://doi.org/10.1016/j.ijrmms.2025.106077 · OSTI ID:2527369

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Commonwealth Scientific and Industrial Research Organisation, Melbourne, VIC (Australia)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

An important task in energy and CO₂ storage (sequestration) in the subsurface is to verify that the surrounding fractures and faults are not activated, acting as leakage pathways. This is achievable through effective and efficient Measurement, Monitoring and Verification (MMV) plans. In this work, two surrogate models are trained to captures dilation (opening) and shear deformation of fractures, and the associated surface deformation. The trained surrogate model, based on conditional Generative-Adversarial Networks (cGAN) receives fracture apertures from dilational fractures together with fracture slips from shear fractures and predicts the combined surface deformation. An inversion algorithm based on Bayesian framework is proposed to identify the geometry of both types of fractures, as well as volume of dilational fractures and deformation moment induced by shear fractures, all from the measured surface deformation data. The inversion algorithm utilises the Differential Evolution (DE) optimisation technique that has the superior performance in finding the global minimum of cost function. The proposed surrogate-assisted inversion successfully inferred the unknown dip, dip direction and the volume of the dilational fractures as well as the induced deformation moment in shear fractures. The model was further tested for the inversion of a field hydraulic fracturing tilt dataset applying different scenarios with varying unknowns to show the model's performance, as well as incorporating shear deformation for better match with the observed data.

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: CSIRO Energy Business Unit; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2527369

Journal Information:: International Journal of Rock Mechanics and Mining Sciences, Journal Name: International Journal of Rock Mechanics and Mining Sciences Vol. 188; ISSN 1365-1609

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Dilation and shear
Fault slip
Generative adversarial networks
Inversion
Machine-learned surrogate model
Tiltmeters

Title: Inferring fracture dilation and shear slip from surface deformation utilising trained surrogate models

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