Regularized seismic full waveform inversion with prior model information

Asnaashari, A.; Brossier, Romain; Garambois, Stéphane; Audebert, F.; Thore, P.; Virieux, Jean

doi:10.1190/geo2012-0104.1

Cited by 175 publications

(105 citation statements)

References 43 publications

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“…However, this uncertainty increases with depth due to the decrease in illumination. On the other hand, because the inversion could be target-oriented toward the reservoir area, particularly in a monitoring case, we can expect to have a lower uncertainty, especially using a priori information (Asnaashari et al, 2013). In all cases, we assume that the uncertainty related to the estimation of quality factors is larger than the uncertainty related to the velocity estimation.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

2016

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The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.

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Section: Sensitivity Analysismentioning

confidence: 99%

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

2016

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“…Then, we perform the acoustic FWI in the time domain, involving all the frequency components of the source wavelet, to invert the acoustic data set. Two FWI are performed to first recover precisely the baseline model (Asnaashari et al, 2011(Asnaashari et al, , 2013 and second, to detect time-lapse changes with a differential approach (Asnaashari et al, 2015). The starting model for the baseline is a smooth version of the true model (Asnaashari et al, 2011).…”

Section: Fwi Resultsmentioning

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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“…This equation expresses the model update vector as the result of the projection of the data residual vector on the model update vectorial space. The hyper parameter α contributes in the filter factor s i 2 s i 2 +α 2 to damp out the small singular value responsible for numerical instability (Menke, 1989;Aster et al, 2005;Asnaashari et al, 2012). The preconditioning vector p assigns a different relative weight to each parameter of the sensitivity matrix in order to guide the inversion towards geologically plausible solutions.…”

Section: Gauss-newton Seismic Inversionmentioning

confidence: 99%

“…The objective or misfit functional accounts for the amplitude and phase characteristics of the wavefield, either as in the full seismogram, or extracted as pre-stack attributes (Fichtner, 2011;JimenezTejero et al, 2015) and it is regularised in order to penalise physically non-meaningful solutions (Menke, 1989;Asnaashari et al, 2012). The least square regularised objective function reads:…”

Section: Gauss-newton Seismic Inversionmentioning

confidence: 99%

Pre-stack full waveform inversion of ultra-high-frequency marine seismic reflection data

Provenzano

Vardy

Henstock

2017

Geophysical Journal International

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SUMMARYThe full waveform inversion (FWI) of seismic reflection data aims to reconstruct a detailed physical properties model of the subsurface, fitting both the amplitude and traveltime of the reflections generated at physical discontinuities in the propagation medium. Unlike reservoirscale seismic exploration, where seismic inversion is a widely adopted remote characterisation tool, ultra high frequency (UHF, 0.2-4.0 kHz) multi-channel marine reflection seismology is still most often limited to a qualitative interpretation of the reflections' architecture. Here we propose an elastic full waveform inversion methodology, custom-tailored for pre-stack UHF marine data in vertically heterogeneous media to obtain a decimetric-scale distribution of Pimpedance, density and Poisson's ratio within the shallow sub-seabed sediments. We address the deterministic multi-parameter inversion in a sequential fashion. The complex trace instantaneous phase is first inverted for the P-wave velocity to make-up for the lack of low-frequency in the data and reduce the non-linearity of the problem. This is followed by a short-offset Pimpedance optimisation and a further step of full offset range Poisson's ratio inversion. Provided that the seismogram contains wide reflection angles (> 40 degrees), we show that it is possible to invert for density and decompose a-posteriori the relative contribution of P-wave velocity and density to the P-impedance. A broad range of synthetic tests is used to prove the potential of the methodology and highlights sensitivity issues specific to UHF seismic. An example application to real data is also presented. In the real case, trace normalisation is applied to minimise the systematic error deriving from an inaccurate source wavelet estimation. G. Provenzano et al.The inverted model for the top 15 meters of the sub-seabed agrees with the local lithological information and core-log data. Thus we can obtain a detailed remote characterisation of the shallow sediments using a multi-channel sub-bottom profiler within a reasonable computing cost and with minimal pre-processing. This has the potential to reduce the need of extensive geotechnical coring campaigns.

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Regularized seismic full waveform inversion with prior model information

Cited by 175 publications

References 43 publications

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

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

Pre-stack full waveform inversion of ultra-high-frequency marine seismic reflection data

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