The Quantification of 4D Noise

Dyce, M.; Whitcombe, David N.; McKenzie, Colin; Hodgson, Linda

doi:10.3997/2214-4609.201405653

Cited by 8 publications

(5 citation statements)

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“…It is defined as

{\rm{NRMS}} = \frac{{{\rm{RMS}}( {{\rm{monitor}}} ) - {\rm{RMS}}( {{\rm{baseline}}} )}}{{{\rm{RMS}}( {{\rm{monitor}} - {\rm{baseline}}} )}}

, where RMS is the root mean square computed vertically along a specified time window. When this is computed along a time window in the overburden above the reservoir, NRMS maps can be interpreted as a measure of time‐lapse non‐repeatability, which is associated with seismic data uncertainties related to seismic noise and time‐lapse acquisition repeatability (Dyce et al., 2004). The time window used for NRMS extraction is shown as yellow lines in Figure 1.…”

Section: Inversion Methodsmentioning

confidence: 99%

“…, where RMS is the root mean square computed vertically along a specified time window. When this is computed along a time window in the overburden above the reservoir, NRMS maps can be interpreted as a measure of time-lapse non-repeatability, which is associated with seismic data uncertainties related to seismic noise and time-lapse acquisition repeatability (Dyce et al, 2004). The time window used for NRMS extraction is shown as yellow lines in Figure 1.…”

Section: Rms(monitor−baseline)mentioning

confidence: 99%

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Bayesian inversion of 4D seismic data to pressure and saturation changes: Application to a west of Shetlands field

Côrte

Amini

MacBeth

2022

Geophysical Prospecting

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A Bayesian inversion methodology is proposed that inverts angle-stacked 4D seismic maps to changes in pressure, water saturation and gas saturation. The inversion method is applied to data from a siliciclastic reservoir in the west of Shetlands, UK continental shelf. We present inversion results for three seismic monitor surveys and demonstrate the added value of pressure-saturation inversion by providing insights into reservoir connectivity and fluid dynamics across 14 years of reservoir production. In these surveys, 4D seismic signals related to waterflood, pressure increase and depletion, and gas exsolution are evident and overlap each other in many regions. To regularize this ill-posed inversion problem, we propose a Bayesian formulation that incorporates spatially variant prior information derived from a history-matched reservoir simulation model and well pressure measurements. The benefit of incorporating these multi-disciplinary data as prior information is demonstrated by comparing to inversion results using a spatially invariant prior. We show that the method takes advantage of the multi-disciplinary prior information to make more precise inferences where the seismic data are most uncertain. This leads to more realistic spatial distributions for the pressure and water-saturation inversion results. The non-uniqueness in this non-linear inversion is studied by analysing uncertainty estimates produced by stochastic sampling of the Bayesian posterior distribution. Posterior standard deviations are observed to be related to the sensitivity of the seismic amplitudes to the changes in each dynamic property as well as the degree of overlap between changes in different dynamic properties. Estimated pressure increases have a posterior standard deviation of approximately 1 MPa, whereas posterior standard deviations for pressure decrease are on average 4 MPa, with higher values applicable to regions of gas exsolution. The posterior standard deviation for estimates of gas saturation change is 0.07 for moderate, visible saturation signals, but 0.005 for low-to-zero gas saturation change. The posterior standard deviation for estimated water saturation change is mainly influenced by overlapping changes in pressure and gas saturation. When water changes dominate the 4D seismic signal, posterior standard deviations are on average 0.05. These values rise to 0.25 in areas where the water change is obscured by pressure or gas-saturation changes.

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“…It is defined as

{\rm{NRMS}} = \frac{{{\rm{RMS}}( {{\rm{monitor}}} ) - {\rm{RMS}}( {{\rm{baseline}}} )}}{{{\rm{RMS}}( {{\rm{monitor}} - {\rm{baseline}}} )}}

Section: Inversion Methodsmentioning

confidence: 99%

Section: Rms(monitor−baseline)mentioning

confidence: 99%

Bayesian inversion of 4D seismic data to pressure and saturation changes: Application to a west of Shetlands field

Côrte

Amini

MacBeth

2022

Geophysical Prospecting

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“…It is already known that time-shifts between the baseline and monitor generate significant 4D noise and errors on the difference amplitudes (Ross & Altan, 1997). We know that this creates geological leakage and magnification of the amplitudes (Dyce et al, 2004). The contribution to the NRMS is a well-documented function of bandwidth, SNR and energy ratio of the data Lecerf et al, 2015).…”

Section: O R C I Dmentioning

confidence: 99%

A review and analysis of errors in post‐stack time‐shift interpretation

MacBeth

Izadian

2023

Geophysical Prospecting

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This study complements two earlier papers on the interpretation and estimation of post‐stack time‐shifts that detail the most popular measurement methods to date. In this current work, the focus is on describing the magnitude and identifying the origin of the uncertainty present in these time‐shift values. We also consider how the underlying assumptions behind conventional time‐shift estimation methods can contribute to the inadequate resolution of the subsurface time‐lapse changes. The various errors fall into three broad categories: (1) those related to intrinsic data limits that cannot be avoided; (2) those associated with seismic measurement methods that can be corrected with some effort; and finally (3) those that arise due to approximations to the wave physics made during the design or implementation of the methods. In the first category are limitations due to sampling rate and signal‐to‐noise ratio, and wavelet interferences resulting from the narrow band nature of the seismic data and the heterogeneous nature of the geology itself. The effects produce errors of typically a fraction of a millisecond, but exceptionally in 4D data with poor repeatability up to 1 ms. In the second category are acquisition and processing errors. These are usually of the order of a few milliseconds but can reach up to 10s of milliseconds and are, therefore, essential to correct. It is possible to correct the effects of tides and seawater variations in marine acquisition if these changes are monitored and measured. Navigation and timing are identified as issues to consider carefully. For land data, daily and seasonal near‐surface variations are still problematic, and source and sensors must be buried to deliver interpretable time‐shifts. The impact of choices made during processing can be significant but is specific to the workflow and dataset and thus cannot be generically assessed. However, the effects from residual multiples can be identified and treated. Of moderate importance is the third category of errors, which consists of four items. Of these, the effect of lateral image shifts and amplitude effects are judged to play a minor role. Deviation from the assumption of vertical ray‐paths during post‐stack analysis appears to be of concern only for reservoirs with noticeably dipping structures and strong contrasts. The effect of pre‐stack variations on the post‐stack measurements remains a topic to be more thoroughly examined, and the conclusion is unclear. Finally, it is apparent that uncertainties in all categories are strongly dependent on the field setting and geographical location.

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Section: Repeatability Measurementsmentioning

confidence: 99%

“…This can be recalculated as new processing or acquisition is introduced to determine the improvement in the seismic difference, or reduction in the noise level. One measure is the CDR (Dyce et al 2004).This attribute computes the RMS amplitudes in a 4D difference volume between two 3D seismic surveys. However, CDR is different from many industry standard repeatability measures, for example normalized root mean square (NRMS), in that it also calibrates the seismic amplitudes to the well reflectivity by matching the spectra of the data and the well over the seismic bandwidth through an application of blueing.…”

mentioning

confidence: 99%

4D acquisition and processing: a North Sea case study on the relative contributions to improved 4D repeatability

Rushmere¹,

Dyce²,

Campbell³

et al. 2010

PGC

Self Cite

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Using a case study from the Mungo Field in the Central North Sea, we investigate the relative impact of acquisition and processing improvements on 4D seismic repeatability. The results show that, while advancements in both have helped to reduce 4D noise, significant noise reduction can be attributed to processing alone. 4D noise can be thought of as any non-production-related amplitudes that are observable on a 4D difference section, and has both random and coherent components. Both are undesirable as they can mask any real 4D signal. A great deal of effort is employed to reduce noise levels by optimizing acquisition and processing between the 3D surveys, which are differenced to highlight the 4D signal. This paper studies the changes introduced by acquisition and processing improvements using the calibrated difference in reflectivity measure.4D noise levels were calculated between three acquisition vintages, upon which two processing flows were applied. An additional application of 'STAR', a 4D post-stack noise reduction process was tested (Zamorouev et al. 2006). Comparisons of repeatability were made using calibrated difference reflectivity (CDR) to highlight improvements through processing, acquisition and combinations of both. For each result a CDR value, map and amplitude spectrum were calculated to represent the 4D noise in that 4D difference volume.For the Mungo 4D Noise Case Study, the total contribution of all improvement is to drive down the 4D noise level by a factor of 3. Acquisition efforts to better match source and receiver locations between baseline and monitor surveys account for approximately one-third of this uplift. The application of STAR accounts for approximately one-third of the total noise reduction, while improvements to the 4D processing flow provide the remaining uplift.A typical method for the analysis of 4D anomalies is the study of 3D seismic difference volumes. The energy in a difference volume is a sum of the 4D signal, random noise which does not match between datasets, and coherent noise caused by the imperfect matching of events. Both forms of noise are undesirable as they can mask any 4D signal that would otherwise be interpretable, as shown in Figure 1. A great deal of effort is employed to reduce 4D noise levels and improve repeatability by optimizing acquisition and processing parameters between 3D surveys, the final objective being to remove both coherent and random noise from the difference volume and improve our ability to interpret the 4D signal. This paper studies the changes introduced by each acquisition and processing step of a typical flow, and attempts to identify where the greatest reduction in 4D noise is achieved. Repeatability measurementsIt is common to use an attribute to measure the level of noise in a 4D difference volume. This can be recalculated as new processing or acquisition is introduced to determine the improvement in the seismic difference, or reduction in the noise level. One measure is the CDR (Dyce et al. 2004).This attribute computes the RMS a...

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The Quantification of 4D Noise

Cited by 8 publications

References 0 publications

Bayesian inversion of 4D seismic data to pressure and saturation changes: Application to a west of Shetlands field

Bayesian inversion of 4D seismic data to pressure and saturation changes: Application to a west of Shetlands field

A review and analysis of errors in post‐stack time‐shift interpretation

4D acquisition and processing: a North Sea case study on the relative contributions to improved 4D repeatability

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