Retrieving 4D Signal in Complex Media Using the Full Waveform Inversion Paradigm

Thore, P.; Allouche, Hassan; Lys, P. O.; Tarrass, I.

doi:10.3997/2214-4609.201400647

Cited by 4 publications

(4 citation statements)

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“…; Thore et al . ; Romdhane, Ravaut and Querendez ; Queißer and Singh ). 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 ; Landrø ) or warping approaches (Williamson, Cherrett and Sexton ).…”

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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“…Thus, inversion to relative velocity change by applying algorithms that operate only vertically is erroneous. This point was made by Thore et al (2010Thore et al ( , 2012, who observed that the 'wake' created by a time-lapse velocity perturbation in an overburden extension scenario for a dipping compacting reservoir did not occur along the vertical direction, but rather in a direction normal to the reservoir formation. To solve for the true velocity perturbations, they introduce a transformation that maps the image data to a pseudo-1D space where 'conventional' time-shift measurement techniques (as defined by the algorithms described in MacBeth et al, 2020) are valid.…”

Section: Non-vertical Ray-pathsmentioning

confidence: 99%

“…This approach does not solve all imaging problems, especially when sequences of bedding dips are non-parallel, and it is limited to a short range of offsets. Audebert and Agut (2014) later implemented the idea of Thore et al (2010) in a slightly different way by working in the depth image domain and considering many local reflectors with small spatial extensions in parallel rather than a large discrete reflector as defined in the original method. The revised method reduces the computational cost and creates results that are localized to the reservoirs of interest.…”

Section: Non-vertical Ray-pathsmentioning

confidence: 99%

A review and analysis of errors in post‐stack time‐shift interpretation

MacBeth

Izadian

2023

Geophysical Prospecting

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This study complements two earlier papers on the interpretation and estimation of post‐stack time‐shifts that detail the most popular measurement methods to date. In this current work, the focus is on describing the magnitude and identifying the origin of the uncertainty present in these time‐shift values. We also consider how the underlying assumptions behind conventional time‐shift estimation methods can contribute to the inadequate resolution of the subsurface time‐lapse changes. The various errors fall into three broad categories: (1) those related to intrinsic data limits that cannot be avoided; (2) those associated with seismic measurement methods that can be corrected with some effort; and finally (3) those that arise due to approximations to the wave physics made during the design or implementation of the methods. In the first category are limitations due to sampling rate and signal‐to‐noise ratio, and wavelet interferences resulting from the narrow band nature of the seismic data and the heterogeneous nature of the geology itself. The effects produce errors of typically a fraction of a millisecond, but exceptionally in 4D data with poor repeatability up to 1 ms. In the second category are acquisition and processing errors. These are usually of the order of a few milliseconds but can reach up to 10s of milliseconds and are, therefore, essential to correct. It is possible to correct the effects of tides and seawater variations in marine acquisition if these changes are monitored and measured. Navigation and timing are identified as issues to consider carefully. For land data, daily and seasonal near‐surface variations are still problematic, and source and sensors must be buried to deliver interpretable time‐shifts. The impact of choices made during processing can be significant but is specific to the workflow and dataset and thus cannot be generically assessed. However, the effects from residual multiples can be identified and treated. Of moderate importance is the third category of errors, which consists of four items. Of these, the effect of lateral image shifts and amplitude effects are judged to play a minor role. Deviation from the assumption of vertical ray‐paths during post‐stack analysis appears to be of concern only for reservoirs with noticeably dipping structures and strong contrasts. The effect of pre‐stack variations on the post‐stack measurements remains a topic to be more thoroughly examined, and the conclusion is unclear. Finally, it is apparent that uncertainties in all categories are strongly dependent on the field setting and geographical location.

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