Seismic Characterization of the Blue Mountain Geothermal Field

Gao, Kai; Huang, Lianjie; Cladouhos, Trenton

doi:10.3390/en16155822

Seismic Characterization of the Blue Mountain Geothermal Field

Journal Article · Sat Aug 05 00:00:00 EDT 2023 · Energies

DOI:https://doi.org/10.3390/en16155822· OSTI ID:1993857

Gao, Kai; Huang, Lianjie;

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1993857

Alternate ID(s):: OSTI ID: 2000909

Report Number(s):: LA-UR--21-23976; PII: en16155822

Journal Information:: Energies, Journal Name: Energies Journal Issue: 15 Vol. 16; ISSN 1996-1073; ISSN ENERGA

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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