Near surface investigation with DAS for CO₂ sequestration and monitoring

Luping Qu

In this thesis, I investigate near-surface seismic properties and several geophysical methods mainly including surface wave dispersion inversion (SWDI) and full waveform inversion (FWI) for CO₂ monitoring, focusing on the capabilities of Distributed Acoustic Sensing (DAS) technology. The study is underpinned by data collected from Newell County Facility, Alberta, Canada, employing seismic data acquired from both surface-deployed and vertical seismic profile (VSP) DAS fiber. DAS data, characterized by broadband and dense spatial sampling, facilitate the extraction of high-resolution near-surface velocity profiles due to their enhanced signal-to-noise ratio and resolution in low-frequency components and multimode dispersion curves. The first segment of the study introduced several types of dispersion curves, explores trans-dimensional (TD) inversion, employing multimode dispersion curves and reversible-jump Markov Chain Monte Carlo (MCMC) sampling to generate probabilistic posterior density (PPD) estimates of model parameters. This approach, augmented with parallel tempering techniques, tested on both synthetic and field DAS data, significantly enhances the interpretation of subsurface velocity profile. By treating model size as a variable, it improves the vertical resolution and decreases uncertainties in shear-wave velocity estimates. The use of advanced mode separation techniques for multimode Rayleigh-wave dispersion curves in DAS data further underscores the approach's eectiveness, yielding inversion results that align well with known lithological data. This research showcases the potential of horizontal DAS data in high-resolution, near-surface investigations.

Additionally, I developed a multi-step multiscale surface wave FWI. Utilizing DASrecorded surface waves, high-resolution S-wave velocity (Vs) and attenuation (quality factor Q_s) models of the near-surface are obtained through FWI, oering improved lateral resolution and depth penetration compared to conventional surface-wave analysis. The inclusion of low-frequency components in DAS data eectively mitigates the cycle skipping challenge commonly associated with FWI, leading to high-resolution VS models that capture lateral variations eectively. I also addressed the challenge of noise in seismic data, particularly its impact on acoustic and elastic FWI models. By incorporating the data covariance matrix into the misfit function, this approach mitigates the eects of noise, improving the accuracy of the FWI models.

Building on these methods, I applied anisotropic FWI with variable density to DASrecorded walk-away VSP data for characterizing subsurface velocity, anisotropy, and density structures. This technique, essential for time-lapse studies of CO₂ injection and storage, proved eective in providing more accurate P-wave velocity, density models, and anisotropy parameters compared to isotropic FWI.

In conclusion, this thesis demonstrates the potential of using DAS technology and advanced geophysical methods for near-surface investigation and CO₂ monitoring. The integration of DAS data with trans-dimensional and varied FWI approaches, alongside noise mitigation strategies, oers a significant step forward in accurate and ecient subsurface characterization, crucial for environmental monitoring and carbon capture and storage initiatives.