Time-Lapse Seismic Full Waveform Inversion Using Improved Cascaded Method

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

doi:10.3997/2214-4609.202229003

Cited by 3 publications

(5 citation statements)

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“…Full-waveform inversion (FWI) has shown its ability to extract high resolution image of the (visco)elastic properties in the subsurface directly from the recorded wavefield [2,3]. Timelapse FWI (TL-FWI) has also shown promising results to monitor property changes in the subsurface, which is important for CO 2 sequestration and oil and gas applications [4][5][6].…”

Section: Motivation and Significancementioning

confidence: 99%

“…Different methods have been proposed to provide accurate time-lapse estimates of the subsurface using TL-FWI. PyFWI allows geophysicists to conduct a time-lapse study using seven methods such as parallel, double-difference [23], cascaded [24], cross-updating [25], simultaneous [25], central-difference [5], and weighted-average inversion [6]. These methods are discussed in more detail in [26].…”

Section: Motivation and Significancementioning

confidence: 99%

“…1: Input: Observed data (d obs ), initial model (m 0 ), and acquisition parameters 2: Initialize OpenCL Switch gradients model [30], which is a well-known model for analyzing the efficiency of FWI algorithms [4,6,25]. Fig.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…This package was first released in January 2022 and based on statistics from PePy website, it has been downloaded 13000 times. The authors used this package in a variety of studies and they published different papers [6,[31][32][33], in addition to the papers under revision or in preparation where PyFWI is presented. This should increase the visibility of this package outside of the research group of the authors.…”

Section: Impactmentioning

confidence: 99%

See 3 more Smart Citations

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

Mardan

Giroux

Fabien‐Ouellet

2023

Preprint

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Section: Motivation and Significancementioning

confidence: 99%

Section: Motivation and Significancementioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

Section: Impactmentioning

confidence: 99%

See 2 more Smart Citations

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

Mardan

Giroux

Fabien‐Ouellet

2023

Preprint

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“…Time-lapse FWI can be performed by considering different combinations of FWI runs. These combinations can be performed sequentially such as for the cases of the cascaded, cross-updating and weighted-average methods (Watanabe et al, 2005;Maharramov et al, 2016;Mardan et al, 2022cMardan et al, , 2023b or in parallel as in the case of the independent and central-difference methods (Plessix et al, 2010;Zhou & Lumley, 2021). In addition to these methods, Maharramov et al (2016) proposed a joint inversion of seismic data from different vintages by introducing the cost function as where subscripts 𝑏 and 𝑚 denote baseline and monitor models.…”

Section: Time-lapse Full-waveform Inversionmentioning

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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