4D seismic data processing issues and examples

Lumley, David; Adams, Donald C.; Meadows, Mark A.; Cole, Steve; Wright, Rich

doi:10.1190/1.1817550

Cited by 51 publications

(15 citation statements)

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“…Seismic acquisition non-repeatability can be caused by unavoidable sourcereceiver positioning errors between surveys, natural variations in the nearsurface soil layer on land (moisture, water table, ground coupling, etc. Lumley et al, 2003). ), and variations in source waveforms, receiver functions and equipment specifi cations.…”

Section: D Seismic Imaging Of Injected Comentioning

confidence: 99%

4D seismic monitoring of CO2 sequestration

Lumley

2010

The Leading Edge

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191

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W e are about to face a surge in the need for geophysical characterization and monitoring of subsurface reservoirs and aquifers for CO 2 sequestration projects. Global energy demand is rising signifi cantly, expected to double over the next 20-30 years, driven by world population increase and the rapid growth of emerging economies. At the current rate of development of alternate energy sources, it is possible that the world may have to rely even more heavily on carbonbased fuels than at present to meet the impending energy demand (Figure 1). With global oil production near its peak or perhaps already in decline, this will place an increased emphasis on coal and LNG (liquid natural gas) in the carbonbased energy mix, and on unconventional hydrocarbon resources like tight gas, coal-bed methane, and heavy-oil tar sands. All of these carbon-based energy sources, especially coal-fi red power plants, LNG, and tar-sand operations, will create a growing supply of excess CO 2 . Irrespective of whether man-made CO 2 emissions are a signifi cant cause of global climate change, or simply well-correlated with global temperature rise, there will be increasing pressure from world governments to reduce the amount of CO 2 emissions to the atmosphere, via policy change (e.g., Kyoto, Copenhagen) or via fi nancial measures (e.g., carbon tax, cap and trade). Capturing industrial CO 2 at its various sources and injecting it into deep geologic formations for longterm storage (sequestration) appears to be one of the most promising methods to achieve signifi cant reductions in atmospheric CO 2 emissions.Th e basic CO 2 sequestration approach will be to capture it at a source (e.g., coal-fi red power plant, LNG facility, or tar-sands operation) and inject it into a deep geologic formation located nearby. Subsurface storage targets include depleted hydrocarbon reservoirs, and saline aquifers. A massive undertaking has already started to locate and rank subsurface reservoir candidates for CO 2 sequestration among the world's sedimentary basins, and future work will require detailed geologic and geophysical site characterization of these reservoirs/aquifers in terms of better defi ning storage capacity (volume, porosity), injectivity (permeability, pressure regime, etc.), and sealing effi ciency (permeability barriers, structural and stratigraphic traps, fault seal, CO 2 trapping mechanisms, CO 2 capillary pressures, geochemistry, etc.). Australia has recently become the fi rst nation in the world to open up off shore exploration leases specifi cally for the purpose of locating potential subsurface CO 2 storage sites in preparation for a future market in CO 2 sequestration ( Figure 2). Th e role for seismicIn addition to site characterization, there will be a huge demand for geophysical monitoring and verifi cation technol-

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Section: D Seismic Imaging Of Injected Comentioning

confidence: 99%

4D seismic monitoring of CO2 sequestration

Lumley

2010

The Leading Edge

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191

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“…We note that the new time-dependency of the problem allows for many possible imaging/inversion approaches (Lumley et al, 2003;Shragge and Lumley, 2013) The current state of the art in 4D seismic is to perform waveequation (time-domain) imaging using the initial estimated (baseline) velocity model to image all time-lapse data sets. We are interested in combining all data sets to simultaneously optimise each image and inversion in a 4D sense, and as an explicit part of the process to estimate the time-dependent velocity/earth model underlying each data set and image.…”

Section: Time-lapse (4d) Seismic Imaging and Inversionmentioning

confidence: 99%

Advanced concepts in active and passive seismic monitoring using full wavefield techniques

Lumley

Shragge

2013

ASEG Extended Abstracts

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“…The first scheme will be a generic processing flow generally applied in time-lapse processing. The second scheme has been modified in this dissertation from the well known simultaneous time-lapse processing discussed in literature such as Lumley et al (2003) and Calvert (2005).…”

Section: Dissertation Organizationmentioning

confidence: 99%

“…Generally, this cross-equalization process reduces the nonrepeatablity effects in seismic acquisition and processing and produces a better timelapse image (Ross et al, 1996;Rickett and Lumley, 2001). Other time-lapse processing techniques are prestack "parallel" processing and "simultaneous" processing described by Lumley et al (2003) which aim to reduce undesired time-lapse differences without match filtering. These are step-by-step time-lapse processing flows available in commercial software, and all of them, poststack and prestack, are collectively called cross-equalization (Lumley et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Other time-lapse processing techniques are prestack "parallel" processing and "simultaneous" processing described by Lumley et al (2003) which aim to reduce undesired time-lapse differences without match filtering. These are step-by-step time-lapse processing flows available in commercial software, and all of them, poststack and prestack, are collectively called cross-equalization (Lumley et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Surface-consistent matching filters for time-lapse processing

Almutlaq

Margrave

2012

SEG Technical Program Expanded Abstracts 2012

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The problem of mismatch between repeated time-lapse seismic surveys remains a challenge, particularly for land acquisition. In this dissertation, we present a new algorithm, which is an extension of the surface-consistent model, and which minimizes the mismatch between surveys, hence improving repeatability.We introduce the concept of surface-consistent matching filters (SCMF) for processing time-lapse seismic data, where matching filters are convolutional filters that minimize the sum-squared error between two signals. Since in the Fourier domain, a matching filter is the spectral ratio of the two signals, we extend the well known surface-consistent hypothesis such that the data term is a trace-by-trace spectral ratio of two datasets instead of only one (i.e. surface-consistent deconvolution). To avoid unstable division of spectra, we compute the spectral ratios in the time domain by first designing tracesequential, least-squares matching filters, then Fourier transforming them. A subsequent least-squares solution then factors the trace-sequential matching filters into four operators: two surface-consistent (source and receiver), and two subsurface-consistent (offset and midpoint).We apply the algorithm to two datasets: a synthetic time-lapse model and field data from a CO 2 monitoring site in Northern Alberta. In addition, two common time-lapse processing schemes (independent processing and simultaneous processing) are compared.We present a modification of the simultaneous processing scheme as a direct result of applying the new SCMF algorithm. The results of applying the SCMF together with the new modified simultaneous processing flow reveal the potential benefit of the method, however some challenges remain, specifically in the presence of random noise.i

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4D seismic data processing issues and examples

Cited by 51 publications

References 1 publication

4D seismic monitoring of CO2 sequestration

4D seismic monitoring of CO2 sequestration

Advanced concepts in active and passive seismic monitoring using full wavefield techniques

Surface-consistent matching filters for time-lapse processing

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