Nonrepeatability effects on time-lapse 4D seismic full-waveform inversion for ocean-bottom node data

Zhou, Wei; Lumley, David

doi:10.1190/geo2020-0577.1

Cited by 17 publications

(6 citation statements)

References 49 publications

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“…This occurs in cases where A i = A j for ∀i = j, or in situations where there is significant noise. This remarkable result was shown to hold for sparsity-promoting denoising of time-lapse field data , for various wavefield reconstructions of randomized simultaneous-source dynamic (towed-array) and static (OBC/OBN) marine acquisitions [Oghenekohwo and Herrmann, 2017b, Kotsi, 2020, Zhou and Lumley, 2021, and for wave-based inversion, including least-squares reverse-time migration and full-waveform inversion [Oghenekohwo, 2017, Oghenekohwo andHerrmann, 2017a]. The observed quality gains in these applications can be explained by improvements in the common component resulting from complementary information residing in non-replicated time-lapse surveys.…”

Section: Monitoring With the Joint Recovery Modelmentioning

confidence: 96%

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Yin¹,

Erdinc²,

Gahlot³

et al. 2022

Preprint

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Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO2 remains within the storage complex. Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images. However, these superior features come, unfortunately, at prohibitive costs and time-intensive efforts potentially rendering extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology where time-lapse images are created by inverting non-replicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of noisy time-lapse seismic datasets are simulated that contain imprints of regular CO2 plumes and irregular plumes that leak. These time-lapse datasets are subsequently inverted to produce time-lapse difference images used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO2 leakage automatically on unseen data and with a reasonable accuracy.

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Section: Monitoring With the Joint Recovery Modelmentioning

confidence: 96%

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Yin¹,

Erdinc²,

Gahlot³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the issue of non-repeatability (NR) can introduce a significant challenge in obtaining suitable timelapse models (Borges et al, 2021). NR issues can cause false time-lapse anomalies, which may be mistakenly interpreted as alterations in the physical characteristics of the subsurface (Zhou and Lumley, 2021b). To address this challenge, the deployment of ocean bottom node (OBN) surveys has gained prominence, representing a practical solution to mitigate NR errors (Yang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…It is important to emphasize that, owing to the inherent nonlinear characteristics of FWI, our investigation also delves into the nonlinear artifacts introduced by the data inversion process. These artifacts can introduce subsurface model changes unrelated to D r a f t reservoir variations, as highlighted in prior researches (Yang et al, 2015;Zhou and Lumley, 2021b;da Silva et al, 2023). The choice to employ FWI is rooted in its standing as a robust seismic inversion method that leverages the comprehensive physical principles embedded within a wave equation (Virieux and Operto, 2009).…”

Section: Introductionmentioning

confidence: 99%

Assessing Time-Lapse Full-Waveform Inversion Strategies in a Brazilian Pre-Salt Setting

Hochwart,

Silva,

Cetale

et al. 2024

Braz J Geophys

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We conducted a comparative study of time-lapse full-waveform inversion (time-lapse FWI) strategies, considering a typical deep-water Brazilian pre-salt setting. This study employed a realistic P-wave model, ocean bottom nodes (OBN) acquisition, noisy data, and a Gaussian anomaly to represent time-lapse model changes. We evaluated the four most commonly used time-lapse FWI schemes. In the first, known as parallel time-lapse FWI, two independent FWI processes are performed from the same initial model, utilizing baseline and monitor datasets. In the second strategy, namely sequential time-lapse FWI, the retrieved baseline model serves as the starting model for inverting monitor data. In both cases, the time-lapse model is derived by subtracting the retrieved baseline model from the retrieved monitor model. The remaining two methods were double-difference and central-difference time-lapse FWI. Our findings demonstrate that all these schemes can detect model variations of 3%. Remarkably, the central-difference time-lapse FWI method demonstrated superior accuracy in producing time-lapse models and, as such, presents itself as a promising strategy for implementation in time-lapse studies within Brazilian pre-salt regions.

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“…The interesting conclusion was that although the data were unanimously considered easy to interpret, overconfidence in the interpreters led to variations in the interpretation, which in turn reflected considerable differences in volumetrics estimation. Apart from that, other uncertainties arising from non-repeatability effects [17], noise, and imaging are present in 4D seismic data. On the plus side, this can provide a great opportunity for the quantitative integration of 4D seismic data in history-matching workflows as it allows access to uncertainty quantification over model parameter estimates and fluid production forecasts analysis (e.g., [18][19][20][21]).…”

Section: Introductionmentioning

confidence: 99%

Four-Dimensional History Matching Using ES-MDA and Flow-Based Distance-to-Front Measurement

Barrela,

Berthet,

Trani

et al. 2023

Energies

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The use of 4D seismic data in history matching has been a topic of great interest in the hydrocarbon industry as it can provide important information regarding changes in subsurfaces caused by fluid substitution and other factors where well data is not available. However, the high dimensionality and uncertainty associated with seismic data make its integration into the history-matching process a challenging task. Methods for adequate data reduction have been proposed in the past, but most address 4D information mismatch from a purely mathematical or image distance-based standpoint. In this study, we propose a quantitative and flow-based approach for integrating 4D seismic data into the history-matching process. By introducing a novel distance parametrization technique for measuring front mismatch information using streamlines, we address the problem from a flow-based standpoint; at the same time, we maintain the amount of necessary front data at a reduced and manageable amount. The proposed method is tested, and its results are compared on a synthetic case against another traditional method based on the Hausdorff distance. The effectiveness of the method is also demonstrated on a semi-synthetic model based on a real-case scenario, where the standard Hausdorff methodology could not be applied due to high data dimensionality.

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Nonrepeatability effects on time-lapse 4D seismic full-waveform inversion for ocean-bottom node data

Cited by 17 publications

References 49 publications

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Assessing Time-Lapse Full-Waveform Inversion Strategies in a Brazilian Pre-Salt Setting

Four-Dimensional History Matching Using ES-MDA and Flow-Based Distance-to-Front Measurement

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