Getting accurate time-lapse information using geology-constrained simultaneous joint migration-inversion

Qu, Shaobo; Verschuur, D.J.

doi:10.1190/segam2016-13964374.1

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…However, this kind of independent processing strategy is usually sensitive to the artefacts generated due to the non‐repeatable uncertainties between different datasets caused by noise, acquisition design and processing ‘tricks’ (such as illumination, preconditioning, smoothing of gradients, etc). Therefore, Qu and Verschuur (2016b, 2016a, 2017, 2018a, 2018b, 2019b, 2019a, 2020) proposed a high‐resolution simultaneous JMI (HR‐S‐JMI) process, which combines JMI with a simultaneous time‐lapse processing strategy. HR‐S‐JMI is parameterized with the baseline reflectivity/velocity and the time‐lapse reflectivity‐/velocity‐difference.…”

Section: Review Of High‐resolution Simultaneous Joint Migration Inversion As An Effective Time‐lapse Imaging Toolmentioning

confidence: 99%

“…Verschuur and Staal (2014) proved that, because JMI is an inversion process, the actual locations of the seismic measurements – providing a sufficient illumination of the subsurface with primaries and multiples is achieved – are not so important, such that the method becomes largely independent of geometry. Qu and Verschuur (2016b, 2016a, 2017, 2018a, 2018b, 2019b, 2019a, 2020) proposed a HR‐S‐JMI process, which combines the joint time‐lapse strategy with the benefit of JMI, together with various regularizations helping to get use of all the information and improve the resolution. The effectiveness of the methodology was successfully demonstrated with a complex synthetic example and a real dataset application.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous joint migration inversion as a high‐resolution time‐lapse imaging method: Feasibility and robustness study

Verschuur

2021

Geophysical Prospecting

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The conventional time‐lapse processing workflow is usually sensitive to the non‐repeatable uncertainties between different vintages caused by noise, acquisition designs and independent processing. Therefore, in order to reduce these non‐repeatable uncertainties, all the datasets are usually acquired from well‐sampled and well‐repeated acquisition surveys, and the independent processing is always carefully tailored to maximally reduce the non‐repeatable uncertainties during processing. Moreover, the conventional time‐lapse analysis method, based on a time‐shift map, is not always a good indicator of the actual velocity differences due to its local one‐dimensional subsurface assumption. In order to relax these rigid requirements and have a better velocity change indicator, a robust high‐resolution simultaneous joint migration inversion was proposed as an effective time‐lapse tool for reservoir monitoring. The method combines a simultaneous data‐processing strategy with the joint migration inversion method. Joint migration inversion is a full wavefield inversion method with a parameterization in terms of reflectivity and propagation velocity. The simultaneous strategy allows the baseline and monitor parameters to communicate and compensate with each other dynamically during inversion, thus, suppressing the non‐repeatable uncertainties during the time‐lapse processing. To investigate the feasibility of using high‐resolution simultaneous joint migration inversion in practice, some numerical experiments are conducted to test the dependence of high‐resolution simultaneous joint migration inversion on the quality of the time‐lapse datasets including the following aspects: random noise; sparse surveys; ocean bottom node versus streamer (different types of monitoring surveys); non‐repeated sources, including source positioning errors and non‐repeated source wavelets; spatial weighting operators in the L2‐norm penalty terms; and sensitivity to weak time‐lapse effects. All the experiments are carried on the basis of a realistic synthetic time‐lapse model based on the Grane field offshore Norway. These experiments show that high‐resolution simultaneous joint migration inversion is robust to random noise, survey sparsity, survey non‐repeatability, source positioning errors and source wavelet discrepancies. Furthermore, high‐resolution simultaneous joint migration inversion remains effective when the spatial weighting operators in the L2‐norm penalty terms are largely relaxed, and high‐resolution simultaneous joint migration inversion is capable of detecting weak time‐lapse changes (e.g. velocity changes down to ±35 m/s).

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Section: Review Of High‐resolution Simultaneous Joint Migration Inversion As An Effective Time‐lapse Imaging Toolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous joint migration inversion as a high‐resolution time‐lapse imaging method: Feasibility and robustness study

Verschuur

2021

Geophysical Prospecting

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“…Qu and Verschuur (, , ) proposed a simultaneous joint migration inversion (S‐JMI) method, which combines JMI with a simultaneous time‐lapse processing strategy. Compared to the sequential JMI inverting each dataset sequentially, this S‐JMI makes it more suitable to handle the case of non‐repeatable surveys.…”

Section: Theory Of Simultaneous Joint Migration Inversionmentioning

confidence: 99%

“…Please note that this paper is an extended version of work published in Qu and Verschuur (, , , , , , ).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Verschuur

2020

Geophysical Prospecting

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A B S T R A C TThe current time-lapse practice is to exactly repeat well-sampled acquisition geometries to mitigate acquisition effects on the time-lapse differences. In order to relax the rigid requirements on acquisition effects, we propose simultaneous joint migration inversion as an effective time-lapse tool for reservoir monitoring, which combines a joint time-lapse data processing strategy with the joint migration inversion method. Joint migration inversion is a full-wavefield inversion method that explains the measured reflection data using a parameterization in terms of reflectivity and propagation velocity. Both the inversion process inside the imaging/inversion scheme and the extra illumination provided by including multiples in joint migration inversion makes the obtained velocity and reflectivity operator largely independent of the utilized acquisition geometry and, thereby, relaxes the strong requirement of non-repeatability during the monitoring. Because simultaneous joint migration inversion inverts for all datasets simultaneously and utilizes various constraints on the estimated reflectivities and velocity, the obtained time-lapse differences have much higher accuracy compared to inverting each dataset separately. It allows the baseline and monitor parameters to communicate with each other dynamically during inversion via a userdefined spatial weighting operator. In order to get more localized time-lapse velocity differences, we further extend the regular simultaneous joint migration inversion to a robust high-resolution simultaneous joint migration inversion process using the timelapse reflectivity difference as an extra constraint for the velocity estimation during inversion. This constraint makes a link between the reflectivity-and the velocity difference by exploiting the relationship between them. We demonstrate the feasibility of the proposed method with a highly realistic synthetic model based on the Grane field offshore Norway and a time-lapse field dataset from the Troll Field.

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“…Even in post-stack seismic data, the existence of noise will affect interpretation result which directly links the seismic images with subsurface oil & gas reservoirs (Wang et al 2011;Chen 2015bChen , 2016Gan et al 2016e;Ren & Tian 2016;Zhang et al 2016e). A successful separation between useful reflection signals and unwanted noise is a long standing problem in the area of seismic data processing and greatly affects the fidelity of subsequent seismic imaging and geophysical inversion, like amplitude-variation-with-offset (AVO) inversion, full waveform inversion, attenuation estimation and geological interpretation (He & Wu 2015;Li et al 2016a;Lines et al 2016;Liu et al 2016a;Mousavi & Langston 2016a,b;Mousavi et al 2016;Qu & Verschuur 2016;Qu et al 2016a;Zhang et al 2016f;Jin et al 2017;Li et al 2017;Ebrahimi et al 2017;Mousavi & Langston 2017). Effectively rejecting the cross-talk interference caused in the simultaneous-source seismic acquisition also plays a significant role in tremendously saving the data recording cost (Berkhout 2008;Qu et al 2014Qu et al , 2015Qu et al , 2016bAbma et al 2015;Chen 2015a;Zu et al 2015Zu et al , 2017aZhou 2017).…”

Section: Introductionmentioning

confidence: 99%

Seismic noise attenuation using an online subspace tracking algorithm

Zhou

Zhang

et al. 2020

Geophysical Journal International

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SUMMARY We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.

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Getting accurate time-lapse information using geology-constrained simultaneous joint migration-inversion

Cited by 13 publications

References 17 publications

Simultaneous joint migration inversion as a high‐resolution time‐lapse imaging method: Feasibility and robustness study

Simultaneous joint migration inversion as a high‐resolution time‐lapse imaging method: Feasibility and robustness study

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Seismic noise attenuation using an online subspace tracking algorithm

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