Some 3D applications of full waveform inversion

Plessix, R. E.; Michelet, Sophie; Rynja, H.; Kuehl, Henning; Perkins, Colin; Maag, J. W. de; Hatchell, Paul

doi:10.3997/2214-4609.20149933

Cited by 21 publications

(13 citation statements)

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“…The time‐lapse changes will be assessed by subtracting the final derived monitor model from that of the baseline (Plessix et al . ). The second approach, called the sequential difference strategy, uses the final baseline model as the starting model for inverting the monitor data set (Asnaashari et al .…”

Section: Introductionmentioning

confidence: 97%

“…Over the past few decades, Full-waveform inversion (FWI) has become a promising technique for velocity model building that reconstructs high-resolution velocity models of the subsurface through the extraction of the full information content of seismic data (Tarantola 1984;Pratt 1999;Virieux and Operto 2009). Since the FWI approach delivers highresolution quantitative images of macro-scale physical parameters, it should appear as a quite attractive tool for monitoring purposes, even if it is not yet widely applied (Gosselet and Singh 2008;Abubakar et al 2009;Plessix et al 2010; Thore et al 2010;Romdhane, Ravaut and Querendez 2012;Queißer and Singh 2013). The FWI technique does not require to identify seismic phases compared to other time-lapse techniques such as amplitude versus offset (AVO) analysis (Tura and Lumley 1999;Landrø 2001) or warping approaches (Williamson, Cherrett and Sexton 2007).…”

Section: Introductionmentioning

confidence: 99%

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Time‐lapse seismic imaging using regularized full‐waveform inversion with a prior model: which strategy?

Asnaashari

Brossier

Garambois

et al. 2014

Geophysical Prospecting

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Full‐waveform inversion is an appealing technique for time‐lapse imaging, especially when prior model information is included into the inversion workflow. Once the baseline reconstruction is achieved, several strategies can be used to assess the physical parameter changes, such as parallel difference (two separate inversions of baseline and monitor data sets), sequential difference (inversion of the monitor data set starting from the recovered baseline model) and double‐difference (inversion of the difference data starting from the recovered baseline model) strategies. Using synthetic Marmousi data sets, we investigate which strategy should be adopted to obtain more robust and more accurate time‐lapse velocity changes in noise‐free and noisy environments. This synthetic application demonstrates that the double‐difference strategy provides the more robust time‐lapse result. In addition, we propose a target‐oriented time‐lapse imaging using regularized full‐waveform inversion including a prior model and model weighting, if the prior information exists on the location of expected variations. This scheme applies strong prior model constraints outside of the expected areas of time‐lapse changes and relatively less prior constraints in the time‐lapse target zones. In application of this process to the Marmousi model data set, the local resolution analysis performed with spike tests shows that the target‐oriented inversion prevents the occurrence of artefacts outside the target areas, which could contaminate and compromise the reconstruction of the effective time‐lapse changes, especially when using the sequential difference strategy. In a strongly noisy case, the target‐oriented prior model weighting ensures the same behaviour for both time‐lapse strategies, the double‐difference and the sequential difference strategies and leads to a more robust reconstruction of the weak time‐lapse changes. The double‐difference strategy can deliver more accurate time‐lapse variation since it can focus to invert the difference data. However, the double‐difference strategy requires a preprocessing step on data sets such as time‐lapse binning to have a similar source/receiver location between two surveys, while the sequential difference needs less this requirement. If we have prior information about the area of changes, the target‐oriented sequential difference strategy can be an alternative and can provide the same robust result as the double‐difference strategy.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Time‐lapse seismic imaging using regularized full‐waveform inversion with a prior model: which strategy?

Asnaashari

Brossier

Garambois

et al. 2014

Geophysical Prospecting

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“…Sirgue et al . (2009) and Plessix et al . (2010) have found that pre‐stack‐migration‐driven velocity models provide a good starting model.…”

Section: Introductionmentioning

confidence: 97%

“…There has been considerable work devoted to constructing such a starting velocity model, with one workflow based on starting velocity models generated from reflection seismic data and a second workflow generated using refraction seismic data. Sirgue et al (2009) and Plessix et al (2010) have found that pre-stack-migrationdriven velocity models provide a good starting model. In contrast, crustal seismologists use lower-fold but longer-offset data amenable to refraction tomography, which serve as their starting model for full waveform inversion (Brenders et al 2008).…”

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confidence: 99%

Laplace-domain waveform inversion versus refraction-traveltime tomography

Bae

Pyun

Shin

et al. 2012

Geophysical Journal International

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SUMMARY Geophysicists and applied mathematicians have proposed a rich suite of long‐wavelength velocity estimation algorithms to construct starting velocity models for subsequent pre‐stack depth migration and inversion. Refraction‐traveltime tomography derives subsurface velocity models from picked first‐arrival traveltimes. In contrast, Laplace‐domain waveform inversion recovers long‐wavelength velocity structure using the weighted amplitudes of first and later arrivals. There are several implementations of first‐arrival traveltime inversion, with most based on ray tracing, and some based on the damped monochromatic wave equation, which accurately represent simple and finite‐frequency first arrivals. Computationally, Laplace‐domain wavefield inversion is quite similar to refraction‐traveltime tomography using damped monochromatic wavefield, but the objective functions used in inversion are radically different. As in classical ray trace‐based traveltime inversion, the objective of refraction‐traveltime tomography using damped monochromatic wavefield is to match the phase (traveltime) of the first arrival of each measured seismic trace. In contrast, the objective of Laplace‐domain wavefield inversion is to match the weighted amplitudes of both first and later arrivals to the weighted amplitudes of the measured seismic trace. Principles of refraction‐traveltime tomography were used to generate velocity models of the earth one century ago. Laplace‐domain waveform inversion is a more recently introduced algorithm and has been less rigorously studied by the seismic research community, with many workers believing it be equivalent to finite‐frequency first‐arrival traveltime tomography. We show that Laplace‐domain waveform inversion is both theoretically and empirically different from finite‐frequency first‐arrival traveltime tomography. Specifically, we examine the Jacobian (sensitivity) kernels used in the two inversion schemes to quantify the different sensitivities (and hence the inversion results) of the two methods. Analysing both surface responses and sensitivity results, we show that the Laplace‐domain waveform inversion's sensitivity to later arrivals provides significantly improved resolution of deeper velocity structure than the first‐arrival traveltime tomography. We demonstrate this capability using numerical inversion examples using a synthetic five‐layer model and the synthetic BP benchmark model. Because of the similar algorithmic structure, Laplace‐domain waveform inversion fits neatly as a starting velocity model pre‐processing component of a larger (multi) frequency‐domain wave equation inversion solution package.

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“…Because the FWI approach delivers high-resolution quantitative images of macroscale physical parameters, it could be used as an attractive technique for monitoring purposes (Gosselet and Singh, 2008;Plessix et al, 2010;Romdhane and Querendez, 2014). QueiBer and Singh (2013) apply acoustic time-lapse FWI to Sleipner CO 2 storage data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of rock physics properties from seismic attributes — Part 2: Applications

et al. 2016

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The estimation of quantitative rock physics properties is of great importance for reservoir characterization and monitoring in CO 2 storage or enhanced oil recovery as an example. We have combined the high-resolution results of full-waveform inversion (FWI) methods with rock physics inversion. Because we consider a generic and dynamic rock physics model, our method is applicable to most kinds of rocks for a wide range of frequencies. The first step allows determination of viscoelastic effective properties, i.e., quantitative seismic attributes, whereas the rock physics inversion estimates rock physics properties (porosity, solid frame moduli, fluid phase properties, or saturation). This two-step workflow is applied to time-lapse synthetic and field cases. The sensitivity tests that we had previously carried out showed that it can be crucial to use multiparameter inputs to accurately recover fluid saturations and fluid properties. However, due to the limited data availability and difficulties in getting reliable multiparameter FWI results, we are limited to acoustic FWI results. The synthetic tests are conclusive even if they are favorable cases. For the first time-lapse fluid substitution synthetic case, we first characterize the rock frame parameters on the baseline model using P-wave velocity estimations obtained by acoustic FWI. Then, we obtain an accurate estimation of fluid bulk modulus from the time-lapse P-wave velocity. In the Marmousi synthetic case, the rock frame properties are accurately recovered for the baseline model, whereas the gas saturation change in the monitor model is not estimated correctly. On the field data example (time-lapse monitoring of an underground blowout in the North Sea), the estimation of rock frame properties gives results on a relatively narrow range, and we use this estimation as a starting model for the gas saturation inversion. We have found that the estimation of the gas saturation is not accurate enough, and the use of attenuation data is then required. However, the uncertainty on the estimation of baseline rock frame properties is not critical to monitor gas saturation changes.

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Some 3D applications of full waveform inversion

Cited by 21 publications

References 0 publications

Time‐lapse seismic imaging using regularized full‐waveform inversion with a prior model: which strategy?

Time‐lapse seismic imaging using regularized full‐waveform inversion with a prior model: which strategy?

Laplace-domain waveform inversion versus refraction-traveltime tomography

Estimation of rock physics properties from seismic attributes — Part 2: Applications

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