Estimation of rock physics properties via FWI of VSP data recorded by accelerometer and fiberoptic sensors

Qi Hu, Matthew Eaid, Scott Keating, Kristopher A. Innanen, Xiaohui Cai

Combining elastic full waveform inversion (FWI) with rock physics can extend the role of FWI from seismic imaging to quantitative prediction and monitoring of reservoir parameters. Distributed Acoustic Sensing (DAS), a rapidly developing seismic acquisition technology, has the potential to be an enabler for such applications of FWI. In this study, we apply a sequential inversion scheme combining elastic FWI and Bayesian rock physics inversion to a vertical seismic profile (VSP) dataset acquired with accelerometers and collocated distributed acoustic sensing (DAS) fiber at the Carbon Management Canada’s Newell County Facility. The goal is to build a baseline model of porosity and lithology parameters to support later monitoring of CO₂ storage. Our key strategies include an effective source approach to cope with near-surface complications, a modeling strategy to simulate DAS data directly comparable to the field data, and a Gaussian mixture approach to capture the bimodality of rock properties. We perform FWI tests on the accelerometer, DAS, and combined accelerometer-DAS data. While our inversion results can accurately reproduce either type of data, the elastic models inverted from the accelerometer data outperform the other two in matching well logs and identifying the target reservoir. We attribute this result to the insignificant advantage of DAS data, in this case, over accelerometer data, which also suffers from single-component measurements and lower signal-to-noise ratios. The porosity and lithology models predicted from the accelerometer elastic models are reasonably accurate at the well location and are geologically meaningful in spatial distribution.