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

Mardan, Amir; Giroux, Bernard; Fabien‐Ouellet, Gabriel

doi:10.2139/ssrn.4330227

Cited by 2 publications

(1 citation statement)

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“…FWI can be performed in any parameterization while the forward modelling is implemented using Equation ( 2). This study is carried out using PyFWI (Mardan et al, 2023a), which is an open-source package that implements FWI in terms of DV parameterization. In the next section, we present the methodology to transfer the earth model from PCS parameterization to DV parameterization for forward modelling.…”

Section: Optimizationmentioning

confidence: 99%

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: Optimizationmentioning

confidence: 99%