Foinaven: 4D processing comes up trumps

Campbell, S.; Lacombe, C.; Brooymans, Rik; Hoeber, H.; White, Simon

doi:10.1190/1.3640527

Cited by 9 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On migrated stacks, with no global matching, cross-equalization, or trace-by-trace matching, we find residual 4D noise on the order of 13.2% ( Figure 6). This compares favorably with recent state-of-the-art North Sea 4D data processing (e.g., Campbell et al, 2011;Helgerud et al, 2012).…”

Section: Application To North Sea Datasupporting

confidence: 73%

“…Techniques such as steerable sources and receivers, feather repeat, and oversampling through the use of extra cables are all designed to maximize the probability of acquiring, on the monitor, traces in the same surface locations as on the baseline. Once time-lapse data are acquired with optimal acquisition repeatability, the data are coprocessed to increase signal similarity as much as possible (e.g., Campbell et al, 2011;Helgerud et al, 2012).…”

Section: D Processing With Variable-depth Streamer Datamentioning

confidence: 99%

See 1 more Smart Citation

An efficient 4D processing flow for variable-depth streamer data

et al. 2014

Self Cite

View full text Add to dashboard Cite

Variable-depth streamer acquisition is now widely used, achieving a bandwidth of more than 6 octaves (2.5 to 200 Hz) and excellent lateral resolution and providing improved impedance and reservoir analysis. This acquisition and processing method is based on towing the streamer deep, using a variable-depth profile optimized to ensure receiver notch diversity at the imaging stage and a deghosting method that takes advantage of the notch diversity. For 4D processing of monitors now acquired with variable-depth cables and with a baseline consisting of a flat towed streamer, we must deal with two processing issues. First, raypaths of traces acquired at the same surface locations differ; second, receiver ghosts lead to different wavelets. A 4D coprocessing flow is introduced that codatums 4D vintages to a common datum early in the sequence. Furthermore, a new deghosting technique is applied to variable-depth and conventional data. This processing flow gives excellent repeatability with 4D noise of less than 10% on a zero-time repeatability test with North Sea data from 2013.Corresponding author: henning.hoeber@cgg.com Figure 8. (a) Postmigration repeatability analysis of conventional towed and variable-depth streamer data after application of codatuming and to a reference datum of 9 m followed by ghost wavefield elimination. There is no 4D effect because the data are acquired within a few days. The residual noise of 9.6% is less than that found with codatuming only (NRMS of 13.2%). (b) The seismic section shows four inlines.

show abstract

Section: Application To North Sea Datasupporting

confidence: 73%

Section: D Processing With Variable-depth Streamer Datamentioning

confidence: 99%

An efficient 4D processing flow for variable-depth streamer data

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…Timeshifts were initially seen as part of the 'cross-equalization' workflow and not necessarily information in their own right (a small time-shift of 2 ms can induce a normalized root mean square (NRMS) as large as 25% in 20 Hz data). How-ever, as 4D seismic surveying has evolved over the past two decades, the quality and repeatability of our post-stack data product have improved considerably (see the examples of Campbell et al, 2011Campbell et al, , 2015Smith et al, 2021). One reason is that acquisition systems are now available with more repeatable geometries.…”

Section: Acquisition and Processing-related Errorsmentioning

confidence: 99%

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

MacBeth

Izadian

2023

Geophysical Prospecting

View full text Add to dashboard Cite

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.

show abstract

“…The success of 4D seismic analysis also relies on the repeatability of the time-lapse surveys. Coprocessed data, in which the nonreservoir-related differences are minimized, give rise to much improved time-lapse results (Rushmere et al, 2010;Campbell et al, 2011). In practice, however, it is very difficult to correct perfectly for the lack of repeatability between surveys (Lumley, 2010).…”

Section: Introductionmentioning

confidence: 99%

Double-difference waveform inversion: Feasibility and robustness study with pressure data

et al. 2015

View full text Add to dashboard Cite

Time-lapse seismic data are widely used to monitor reservoir changes. Qualitative comparisons between baseline and monitor data sets or image volumes provide information about fluid and pressure effects within the reservoir during production. However, to perform real quantitative analysis of such reservoir changes, quantitative estimates of the elastic parameters are required as input parameters to rock-physics-based reservoir models. Full-waveform inversion has been proposed as a potential tool for retrieving subsurface properties, such as P-and S-wave velocities and density by fitting simulated waveforms to seismic data. An extension of this method to time-lapse applications seems straightforward, but, in fact, it requires more tailored processes such as double-difference waveform inversion (DDWI). We used realistic 2D synthetic pressure data examples to compare the performance of DDWI with that of two other inversion schemes: one using the same starting model for both inversions and the other starting the monitor inversion with the final baseline inversion model. The data simulation and inversion were based on acoustic theory. Although P-wave velocity changes were reliably recovered by each inversion method, DDWI was found to deliver the best results when perfectly repeated surveys were used. However, differencing the baseline and monitor data sets, as required by DDWI, could be found to be sensitive to the presence of survey nonrepeatability. To investigate the feasibility of using DDWI in practice, the dependence of DDWI on the quality of the baseline models and its robustness to survey nonrepeatability were studied with numerical tests. Various types of nonrepeatability were considered separately in the synthetic tests, including random noise, acquisition geometry mismatch, source wavelet discrepancy, and overburden velocity changes. A study of the correlation between the levels and types of nonrepeatability and the resulting contamination of the inversion results found that, for pressure data, DDWI was capable of inverting reliably for P-wave velocity changes under realistic survey nonrepeatability conditions.

show abstract

Foinaven: 4D processing comes up trumps

Cited by 9 publications

References 8 publications

An efficient 4D processing flow for variable-depth streamer data

An efficient 4D processing flow for variable-depth streamer data

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

Double-difference waveform inversion: Feasibility and robustness study with pressure data

Contact Info

Product

Resources

About