Estimation of water layer correction in shallow time-lapse streamer data sets

Beng ONG; Jeshurun Hembd; Arjun Srinivasan; Dez Chu; David H. Johnston

doi:10.1190/segam2015-5866701.1

Estimation of water layer correction in shallow time-lapse streamer data sets

Beng ONG, Jeshurun Hembd, Arjun Srinivasan, Dez Chu, David H. Johnston

Department of Chemistry

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

Time-lapse seismic surveys are intended to measure changes in the subsurface related to reservoir production. Accurate estimation of the reservoir level requires corrections to layers above the reservoir, including the water layer. Water layer changes can be attributed to ocean tides and water velocity changes, which require offset- and depth-dependent timing corrections. These can be difficult to estimate in shallow water marine streamer data sets; thus, we developed a completely data-driven method that reduces timing differences and is effective in both shallow and deep water.

Original language	English
Pages (from-to)	5394-5398
Number of pages	5
Journal	SEG Technical Program Expanded Abstracts
Volume	34
DOIs	https://doi.org/10.1190/segam2015-5866701.1
Publication status	Published - 2015
Event	SEG New Orleans Annual Meeting, SEG 2015 - New Orleans, United States Duration: 18 Oct 2011 → 23 Oct 2011

Scopus Subject Areas

Geotechnical Engineering and Engineering Geology
Geophysics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1190/segam2015-5866701.1

Cite this

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abstract = "Time-lapse seismic surveys are intended to measure changes in the subsurface related to reservoir production. Accurate estimation of the reservoir level requires corrections to layers above the reservoir, including the water layer. Water layer changes can be attributed to ocean tides and water velocity changes, which require offset- and depth-dependent timing corrections. These can be difficult to estimate in shallow water marine streamer data sets; thus, we developed a completely data-driven method that reduces timing differences and is effective in both shallow and deep water.",

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AU - ONG, Beng

AU - Hembd, Jeshurun

AU - Srinivasan, Arjun

AU - Chu, Dez

AU - Johnston, David H.

PY - 2015

Y1 - 2015

N2 - Time-lapse seismic surveys are intended to measure changes in the subsurface related to reservoir production. Accurate estimation of the reservoir level requires corrections to layers above the reservoir, including the water layer. Water layer changes can be attributed to ocean tides and water velocity changes, which require offset- and depth-dependent timing corrections. These can be difficult to estimate in shallow water marine streamer data sets; thus, we developed a completely data-driven method that reduces timing differences and is effective in both shallow and deep water.

AB - Time-lapse seismic surveys are intended to measure changes in the subsurface related to reservoir production. Accurate estimation of the reservoir level requires corrections to layers above the reservoir, including the water layer. Water layer changes can be attributed to ocean tides and water velocity changes, which require offset- and depth-dependent timing corrections. These can be difficult to estimate in shallow water marine streamer data sets; thus, we developed a completely data-driven method that reduces timing differences and is effective in both shallow and deep water.

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U2 - 10.1190/segam2015-5866701.1

DO - 10.1190/segam2015-5866701.1

M3 - Conference article

AN - SCOPUS:85018948655

SN - 1052-3812

VL - 34

SP - 5394

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JO - SEG Technical Program Expanded Abstracts

JF - SEG Technical Program Expanded Abstracts

T2 - SEG New Orleans Annual Meeting, SEG 2015

Y2 - 18 October 2011 through 23 October 2011

ER -

Estimation of water layer correction in shallow time-lapse streamer data sets

Abstract

Scopus Subject Areas

UN SDGs

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