Elastic full-waveform inversion with rock-physics constraints

Hu, Qi; Innanen, Kristopher A.

doi:10.1190/segam2021-3594471.1

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…However, reconstructing the clay content seems problematic, and the interfaces in this parameter are not recovered sharply (Figures 4b and 5b). Although Hu and Innanen (2021) showed that the C model can be estimated better by considering a parameter‐relation regularization, this type of regularization is not taken into account in this study. The parameter‐relation regularization allows us to regularize the inversion based on the relation between two parameters in the field.…”

Section: Numerical Analysismentioning

confidence: 98%

Monitoring fluid saturation in reservoirs using time‐lapse full‐waveform inversion

Mardan

Giroux

Fabien‐Ouellet

et al. 2023

Geophysical Prospecting

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Monitoring the rock-physics properties of the subsurface is of great importance for reservoir management. For either oil and gas applications or CO 2 storage, seismic data are a valuable source of information for tracking changes in elastic properties which can be related to fluids saturation and pressure changes within the reservoir. Changes in elastic properties can be estimated with time-lapse full-waveform inversion. Monitoring rock-physics properties, such as saturation, with time-lapse full-waveform inversion is usually a two-step process: first, elastic properties are estimated with full-waveform inversion, then, the rock-physics properties are estimated with rock-physics inversion. However, multiparameter time-lapse full-waveform inversion is prone to crosstalk between parameter classes across different vintages. This leads to leakage from one parameter class to another, which, in turn, can introduce large errors in the estimated rock-physics parameters. To avoid inaccuracies caused by crosstalk and the two-step inversion strategy, we reformulate time-lapse full-waveform inversion to estimate directly the changes in the rock-physics properties. Using Gassmann's model, we adopt a new parameterization containing porosity, clay content, and water saturation. In the context of reservoir monitoring, changes are assumed to be induced by fluid substitution only. The porosity and clay content can thus be kept constant during time-lapse inversion. We compare this parameterization with the usual density-velocity parameterization for different benchmark models. Results indicate that the proposed parameterization eliminates crosstalk between parameters of different vintages, leading to more accurate estimation of saturation changes. We also show that using the parameterization based on porosity, clay content, and water saturation, the elastic changes can be monitored more accurately.Keywords full-waveform inversion • CO 2 monitoring • time-lapse inversion • reservoir monitoring • rock-physics monitoring

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Section: Numerical Analysismentioning

confidence: 98%

Monitoring fluid saturation in reservoirs using time‐lapse full‐waveform inversion

Mardan

Giroux

Fabien‐Ouellet

et al. 2023

Geophysical Prospecting

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show abstract

“…An additional challenge is the crosstalk between the parameters [3,14]. To make the problem better posed, four common regularization methods such as Tikhonov, total-variation, prior-information, and parameters relationship [4,[15][16][17] are integrated into this package. The most effective methods to mitigate the problem of crosstalk are performing the inversion using an optimal parameterization [14,18] and using the Hessian for optimization [19,20].…”

Section: Motivation and Significancementioning

confidence: 99%

Pyfwi: A Python Package for Full-Waveform Inversion and Reservoir Monitoring

Mardan

Giroux

Fabien‐Ouellet

2023

Preprint

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Feasibility of seismic time-lapse monitoring of CO2 with rock physics parametrized full waveform inversion

Grana

Innanen

2022

Geophysical Journal International

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Summary Carbon capture and storage is an important technology for greenhouse gas mitigation. Monitoring of CO2 storage should, in addition to locating the plume, provide quantitative information on CO2 saturation. We propose a full waveform inversion (FWI) algorithm for the prediction of the spatial distribution of CO2 saturation from time-lapse seismic data. The methodology is based on the application of a rock-physics parameterized FWI scheme that allows for direct updating of reservoir properties. We derive porosity and lithology parameters from baseline data and use them as input to predict CO2 saturation from monitor data. The method is tested on synthetic time-lapse data generated for the Johansen formation model. Practical issues associated with field data applications, such as acquisition limitations, construction of the initial model, noise, and uncertainty in the rock physics model, are taken into account in the simulation. The results demonstrate the robustness of our approach for reconstructing baseline and monitor models. We also illustrate the potential of the approach as compared to conventional two-step inversion algorithms, in which an elastic FWI prediction of velocities and density is followed by rock physics inversion.

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Elastic full-waveform inversion with rock-physics constraints

Cited by 5 publications

References 21 publications

Monitoring fluid saturation in reservoirs using time‐lapse full‐waveform inversion

Monitoring fluid saturation in reservoirs using time‐lapse full‐waveform inversion

Pyfwi: A Python Package for Full-Waveform Inversion and Reservoir Monitoring

Feasibility of seismic time-lapse monitoring of CO2 with rock physics parametrized full waveform inversion

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