Deblending of OBN ultralong-offset simultaneous source acquisition

Yi, Xuan; Malik, Raheel; Zhang, Zhigang; Guo, Manhong; Huang, Yi

doi:10.1190/segam2020-3428390.1

Cited by 4 publications

(1 citation statement)

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“…To avoid aliasing, we jitter sample [Herrmann and Hennenfent, 2008] the receiver positions of 64 hydrophones located at a depth of 120m with an average spacing of 250m horizontally. To reduce the costs on the source side, we propose non-replicated continuous simultaneous source shooting, which after deblending [Li et al, 2013, Xuan et al, 2020 yields 1272 sources with a dense source sampling of 12.5m horizontally. To mimic the challenges related to active-source acquisition, we vary for each survey the tow-depth of sources uniformly randomly from 5m to 15m.…”

Section: Time-lapse Data Acquisitionmentioning

confidence: 99%

Compressive time-lapse seismic monitoring of carbon storage and sequestration with the joint recovery model

Yin

Louboutin

Herrmann

2021

Preprint

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Time-lapse seismic monitoring of carbon storage and sequestration is often challenging because the time-lapse signature of the growth of CO2 plumes is weak in amplitude and therefore difficult to detect seismically. This situation is compounded by the fact that the surveys are often coarsely sampled and not replicated to reduce costs. As a result, images obtained for different vintages (baseline and monitor surveys) often contain artifacts that may be attributed wrongly to time-lapse changes. To address these issues, we propose to invert the baseline and monitor surveys jointly. By using the joint recovery model, we exploit information shared between multiple time-lapse surveys. Contrary to other time-lapse methods, our approach does not rely on replicating the surveys to detect time-lapse changes. To illustrate this advantage, we present a numerical sensitivity study where CO2 is injected in a realistic synthetic model. This model is representative of the geology in the southeast of the North Sea, an area currently considered for carbon sequestration. Our example demonstrates that the joint recovery model improves the quality of time-lapse images allowing us to monitor the CO2 plume seismically.

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Section: Time-lapse Data Acquisitionmentioning

confidence: 99%