Time-lapse Seismic without Repetition - Reaping the Benefits from Randomized Sampling and Joint Recovery

Oghenekohwo, Felix; Esser, Ernie; Herrmann, Felix J.

doi:10.3997/2214-4609.20141478

Cited by 4 publications

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“…Subsequently, estimates of x 1 and x 2 are computed from recovery of these three components. Under certain assumptions, Oghenekohwo et al (2014) and Wason et al (2015) showed that (i) independent non-replicated time-lapse surveys (A 1 = A 2 ) yield better recoveries for the vintages when processed jointly with the JRM and the results are superior to those obtained with the IRS and (ii) when the surveys are calibrated, we get results that are more or less equivalent to results when repeating the surveys exactly. This is case for time lapse while vintages improve.…”

Section: Introductionmentioning

confidence: 96%

“…Following remarkable success with CS-based acquisition technology, Oghenekohwo et al (2014) proposed randomized sampling for time-lapse acquisition and a joint recovery model (JRM) to address challenges related to cost of the surveys and difficulty in replicating surveys. The JRM which derives from distributed compressive sensing (DCS) (Baron et al, 2009), recovers a common part and innovations with respect to the common part, leading to improved recovery of time-lapse vintages when the surveys are not replicated.…”

Section: Introductionmentioning

confidence: 99%

“…In the noise-free case, we achieve this objective by solving the sparsity-promoting program: x = arg min x x 1 subject to y = Ax, for j ∈ 1, 2. Rather than follow an independent recovery strategy (IRS), where we recover x 1 and x 2 separately, we adopt the joint recovery model (JRM) proposed by Oghenekohwo et al (2014) for processing time-lapse data using shared information among the vintages. This model derives from distributed compressed sensing (DCS) (Baron et al, 2009) and instead involves the following system of equations:…”

Section: Introductionmentioning

confidence: 99%

“…Here, a receiver gather, for instance, shows overlapping shots that are unraveled and interpolated during the seismic data recovery stage (Wason and Herrmann, 2013). In the noise-free case and without calibration, Oghenekohwo et al (2014) and Wason et al (2015) showed that independent and non-replicated acquisitions lead to improved recovery of the time-lapse vintages using the JRM over IRS. Their observations motivate us to investigate the role of calibration errors and noise within CS-based time-lapse acquisition design.…”

Section: Introductionmentioning

confidence: 99%

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Improved Time-lapse Data Repeatability with Randomized Sampling and Distributed Compressive Sensing

Herrmann¹,

Oghenekohwo²

2017

Proceedings

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Recently, new ideas on randomized sampling for time-lapse seismic acquisition have been proposed to address some of the challenges of replicating time-lapse surveys. These ideas, which stem from distributed compressed sensing (DCS) led to the birth of a joint recovery model (JRM) for processing time-lapse data (noise-free) acquired from non-replicated acquisition geometries. However, when the earth does not change-i.e. no time-lapse-the recovered vintages from two non-replicated surveys should show high repeatability measured in terms of normalized RMS, which is a standard metric for quantifying time-lapse data repeatability. Under this assumption of no time-lapse change, we demonstrate improved repeatability (with JRM) of the recovered data from non-replicated random samplings, first with noisy data and secondly in situations where there are calibration errors i.e., where the acquisition parameters such as source/receiver coordinates are not precise.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Time-lapse Data Repeatability with Randomized Sampling and Distributed Compressive Sensing

Herrmann¹,

Oghenekohwo²

2017

Proceedings

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Handling gaps in acquisition geometries — Improving Marchenko-based imaging using sparsity-promoting inversion and joint inversion of time-lapse data

2021

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Marchenko focusing and imaging are novel methods for correctly handling multiple scattered energy while processing seismic data. However, strict requirements in the acquisition geometry, specifically co-location of sources and receivers as well as dense and regular sampling, currently constrain their practical applicability. We reformulate the Marchenko equations to handle the case where there are gaps in the source geometry while receiver sampling remains regular (or the opposite, by means of reciprocity). Using synthetic data based on a velocity model that produces strong interbed multiples, we test different solvers for the newly formulated inversion problem and we compare these results to results obtained by applying standard Marchenko inversion to a previously reconstructed dataset. When using the unreconstructed dataset, the ability of the Marchenko equations to retrace multiple reflected energy deteriorates. Sparsity-promoting Marchenko inversion, while improving the appearance of focusing functions, barely decreases multiple leakage in gathers and does not visibly improve the final image when compared to standard least-squares inversion. On the other hand, reconstructing the wavefield in advance restores the proper functioning of the Marchenko methods. Further, we test a joint inversion technique designed for time-lapse data with non-repeated geometries and originally intended to be solved using sparsity-promoting inversion. Motivated by our previous results, we compare images produced by this method to images produced by solving the same joint inversion problem without sparsity constraint. We find that the joint inversion alone hardly improves the resulting images but, when combined with the sparsity constraint, it leads to better noise and multiple suppression and produces a clean time-lapse image. Overall, none of the results from sparsity-promoting inversion techniques match the results obtained when reconstructing the wavefield in advance. We show that this can be explained by the comparatively slow convergence rate of sparsity-promoting Marchenko inversion.

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Randomized sampling without repetition in time-lapse seismic surveys

Oghenekohwo

Kumar

Herrmann

2014

SEG Technical Program Expanded Abstracts 2014

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Time-lapse Seismic without Repetition - Reaping the Benefits from Randomized Sampling and Joint Recovery

Cited by 4 publications

References 0 publications

Improved Time-lapse Data Repeatability with Randomized Sampling and Distributed Compressive Sensing

Improved Time-lapse Data Repeatability with Randomized Sampling and Distributed Compressive Sensing

Handling gaps in acquisition geometries — Improving Marchenko-based imaging using sparsity-promoting inversion and joint inversion of time-lapse data

Randomized sampling without repetition in time-lapse seismic surveys

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