Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness

Li, Yuanyuan; Guo, Qiang; Alkhalifah, Tariq; Kazei, Vladimir

doi:10.1190/segam2020-3420515.1

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Then, they employed the redatumed data as input for elastic FWI, demonstrating its practicality and its capacity to lower computational expenses. Guo and Alkhalifah (2020) [56] also recognized the impractical costs associated with implementing FWI using a full-scale model. As a solution, they employed redatuming techniques to concentrate on velocity inversion within the target area, leading to enhanced efficiency and reduced memory requirements.…”

Section: Full-scale Target-orientedmentioning

confidence: 99%

“…Recognizing that the velocity model building process is exceedingly time-consuming within seismic processing and imaging procedures, seismic redatuming is employed to effectively decrease the overall time required for generating precise velocity models. For example, it has found applications in time-lapse Full Waveform Inversion [37,38], datum-based Full Waveform Inversion [39,40], and target-oriented elastic Full Waveform Inversion [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

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Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method

Dzulkefli,

Abdul Latiff,

Hashim

et al. 2023

Applied Sciences

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A major challenge for the oil and gas industry is expediting the exploration geophysics activity. It is necessary to produce fast and accurate image of the targeted hydrocarbon reservoir, yet it involves extensive dataset due to the target location lies in deeper subsurface. Therefore, target-oriented oil and gas-reservoir imaging is currently seen as more attractive than conventional full-volume migration, in terms of computation efficiency. Through the integration of seismic redatuming with Kirchhoff migration, we presented a target-oriented imaging workflow that can effectively reduce computational cost while simultaneously enhancing the accuracy of the migrated image. Kirchhoff migration is renowned for being one of the simplest and flexible migration techniques, making it straightforward to implement. However, this migration approach is typically avoided in the presence of complex geology, as it tends to introduce artifacts in the images, thereby disrupting the interpretation beneath the complex overburden. Therefore, this paper highlighted the advantages of utilizing seismic redatuming to retrieve data beneath the overburden, followed by the utilization of the redatumed data as input for Kirchhoff migration. With an improved signal to noise ratio, the primary reflection events were amplified, providing clearer representation of the subsurface structure within the hydrocarbon reservoir or targeted area. Additionally, we utilized the benefits of Kirchhoff migration and explored the feasibility of performing patch-based migration in several areas of interest beneath the salt layer. This approach aimed to reduce both the turnaround time and the associated seismic migration costs. The developed workflow then was subsequently put to the test and applied to both a sub-salt SEG Advanced Modeling (SEAM) model and field data situated in East Malaysia. Based on the outcomes of the target-oriented migration, the proposed methodology demonstrated its capability to generate better quality and instant migrated images within the designated region. Furthermore, the feasibility of target-oriented imaging with Kirchhoff migration was substantiated through the integration of seismic redatuming, resulting in significant benefits towards seismic processing, imaging, and interpretation.

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Section: Full-scale Target-orientedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method

Dzulkefli,

Abdul Latiff,

Hashim

et al. 2023

Applied Sciences

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Target-oriented time-lapse waveform inversion using deep learning-assisted regularization

Alkhalifah

Guo

2021

GEOPHYSICS

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Detection of the property changes in the reservoir during injection and production is important. However, the detection process is very challenging using surface seismic surveys because these property changes often induce subtle changes in the seismic signals. The quantitative evaluation of the subsurface property obtained by full waveform inversion (FWI) allows for better monitoring of these time-lapse changes. However, high-resolution inversion is usually accompanied with a large computational cost. Besides, the resolution of inversion is limited by the bandwidth and aperture of time-lapse seismic data. We apply a target-oriented strategy through seismic redatuming to reduce the computational cost by focusing our high-resolution delineation on a relatively small zone of interest. The redatuming technique generates time-lapse virtual data for the target-oriented inversion. Considering the injection and production wells are often present in the target zone, we can incorporate the well velocity information to the time-lapse inversion by using regularization to complement the resolution and illumination at the reservoir. We use a deep neural network (DNN) to learn the statistical relationship between the inverted model and the facies interpreted from well logs. The trained network is employed to map the property changes extracted from the wells to the target inversion domain. We then perform another time-lapse inversion, in which we fit the predicted data difference to the redatumed one from observation, as well as fit the model to the predicted velocity changes. The numerical results demonstrate that the proposed method is capable of inverting for the time-lapse property changes effectively in the target zone by incorporating the learned model information from well logs.

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Target-oriented high-resolution elastic full-waveform inversion with an elastic redatuming method

Alkhalifah

2022

GEOPHYSICS

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Characterizing the elastic properties in deep-buried reservoirs beneath complex overburden structures remains challenging for seismic inversion. Elastic full waveform inversion (FWI) is capable of quantitatively estimating the subsurface elastic properties with reasonably high resolution. However, elastic FWI using high frequencies is computationally expensive because fine discretization is required to stabilize the wavefield simulation. Additionally, it is challenging for elastic FWI to obtain high-resolution inversion results of the deep targets due to complex overburden structures and limited energy illumination of the target of interest. To overcome these limitations, we propose a target-oriented high-resolution elastic FWI scheme by using estimated elastic data for a virtual survey deployed just above a zone of interest. Specifically, we present an elastic redatuming approach to retrieve virtual elastic data by solving an iterative least-squares data fitting problem. In addition to the elastic multicomponent data recorded at the acquisition surface, an estimate of the overburden model is required. A synthetic data example shows that an overburden model estimated by elastic FWI with a low-frequency band enables our elastic redatuming approach to reconstruct the virtual elastic data representing the seismic reflection response from the target zone. We then perform regularized elastic FWI by using the redatumed elastic data to estimate the elastic properties in the target zone with reduced computational cost. In the numerical examples, the Marmousi2 synthetic data and 2D Volve field data are used to demonstrate the feasibility and practicality of the proposed method.

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Target-oriented time-lapse waveform inversion using redatumed data: Feasibility and robustness

Cited by 5 publications

References 24 publications

Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method

Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method

Target-oriented time-lapse waveform inversion using deep learning-assisted regularization

Target-oriented high-resolution elastic full-waveform inversion with an elastic redatuming method

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