We Dome1 14 Model Building in Complex Geological ...
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82nd EAGE Conference & Exhibition 2020
8-11 December 2020, Amsterdam, The Netherlands
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Model Building in Complex Geological Situations Using Low-Frequency Data from an Optimised Airgun Technology Based Source J. Brittan1*, Y. Cobo1, P. Farmer1, C. Wang1, D. Brookes1 1 ION
Summary The emergence of seismic inversion techniques such as full-waveform inversion (FWI) has provided a significant increase in the resolution that may be achieved in models of physical earth properties. It has become clear that achieving optimal results i.e. a broad band of model wavenumbers implies data with good azimuthal coverage, long offsets, full frequency coverage and, ideally, inversion of multiscattered energy. Modern ocean-bottom node surveys commonly provide good azimuthal and offset coverage, however such surveys often still do not provide good quality data at low frequencies. This is due to deficiencies in both low frequency energy generation by the source and low frequency energy detection at the receiver. Consequently, there has been considerable recent interest in improving the low-frequency performance of seismic sources. In this paper we discuss a survey recently conducted in the Western Gulf of Mexico in which a new seismic source based on airgun technology was utilized with an array of sparse ocean-bottom nodes. We show that the increase in signal-to-noise at low frequencies (1.5-4Hz) that is achieved by the new source (relative to a conventional multi-airgun array) leads to an improvement in the resulting velocity model derived using FWI.
82nd EAGE Conference & Exhibition 2020
8-11 December 2020, Amsterdam, The Netherlands
Introduction
The emergence of seismic inversion techniques such as full-waveform inversion (FWI) has provided a
significant increase in the resolution that may be achieved in models of physical earth properties. With
the widespread use of these techniques, it has become clear that achieving optimal results with wave-
equation based model-building methods implies that the acquired data should have certain
characteristics. As discussed by Alkhalifah et al. (2018) the seismic data required for successful
recovery of a broad band of model wavenumbers implies, in 3-D, good azimuthal coverage, long offsets,
full frequency coverage and, ideally, inversion of multiscattered energy. Leaving aside the difficulties
of inverting for high-order scattered energy, modern ocean-bottom node surveys commonly provide
good azimuthal and offset coverage, however such surveys often still do not provide good quality data
at low frequencies. This is due to deficiencies in both low frequency energy generation by the source
and low frequency energy detection at the receiver.
Consequently, there has been considerable recent interest in improving the low-frequency performance
of seismic sources (e.g. Brenders et al., 2018). We recently conducted a survey in the Western Gulf of
Mexico in which a new seismic source based on airgun technology was utilized with an array of sparse
ocean-bottom nodes (Brittan et al., 2019). In this paper we show that the increase in signal-to-noise at
low frequencies (1.5-4Hz) that is achieved by the new source (relative to a conventional multi-airgun
array) leads to an improvement in the resulting velocity model derived using FWI.
Why do we think low frequencies matter?
In Figure 1 we show a synthetic experiment that illustrates the contribution of data with low temporal
frequencies to the model building process. The synthetic model (Figure 1(a)) has characteristics typical
of salt regimes worldwide in that the sedimentary section above the salt is relatively simple, the salt
body is complex and there are low-velocity structures sub-salt.
Figure 1. (a) Section of the EAGE 2004 workshop synthetic model (Billette and Brandsberg-Dahl, 2005)
showing a complex salt body and low-velocity sub-salt structure. (b) The result of using a standard
multiscale, least-squares FWI algorithm (with offsets up to 30km) to recover the velocity structure if the
minimum frequency used in the inversion is 3 Hz. (c) The result of using the same standard, multiscale,
least-squares FWI algorithm to recover the velocity structure while only using frequencies between 0.5
and 2 Hz.
a b
c
82nd EAGE Conference & Exhibition 2020
8-11 December 2020, Amsterdam, The Netherlands
We choose to start, in a somewhat unrealistic but usefully indicative manner, with an initial model that
does not contain any salt or sub-salt structure. If we use a standard, multiscale FWI algorithm with a
least-squares objective function (Brittan and Jones, 2019 and references therein) and choose a starting
frequency that is typical of the limit of acceptable S/N in modern OBS with conventional sources (3 Hz)
we find that the algorithm recovers the shallow sediments and top-salt well but fails to insert the bulk
of the salt body and any accurate sub-salt structure (Figure 1(b)). However, if the data contains very
low-frequency energy (between 0.5 and 2Hz) – the same FWI algorithm and approach can recover both
the salt body and the sub-salt structure (Figure 1(c)). While 0.5Hz is very difficult in practice (due to
both the increase in ambient noise at low frequencies in all environments and the practical, mechanical
difficulties in generating large amounts of low-frequency energy) – this synthetic experiment illustrates
the value of low-frequency data to the inversion process – in both information content and resistance to
cycle-skipping.
A new seismic source
We have developed a new seismic source based on airgun technology that improves the signal-to-noise
in the low-frequency part of the seismic spectrum (typically showing an improvement in the 1.5-5 Hz
range relative to a conventional multi-gun seismic array (Brittan et al., 2019). The improvements are
achieved by careful control of the oscillations of the bubble created by the source. Key factors that
control both the creation and oscillations of this bubble are the final pressure of the source chamber, the
volume of the source chamber, the depth of tow and the way the air is released from the chamber. In
Figure 2 we compare examples from the input data for the FWI algorithm suite used in the modelling.
These data were collected in a survey in the Western Gulf of Mexico in which the both the new source
(volume 6000 cubic inch) and a conventional 5110 cubic inch multi-gun source array were recorded on
the same ocean-bottom node instruments. It should be noted that the OBN used in this survey were
commercial, industry standard units with 3 Hz roll-off hydrophones and 10 Hz roll-off geophones. The
data from the new source (Figure 2 right) shows considerable improvement in first arrival continuity at
low frequencies (<5Hz) than that of the conventional multi-gun seismic array.
Figure 2. Comparison of data from (left) the conventional 5110 cubic inch multi-gun array and (right)
the new source used in the survey. The data shown are receiver gathers from the same ocean-bottom
node. The data have been low-pass filtered in the frequency band to show the 4Hz, are shown with
offsets up to 45km and have been equalised based on the first arrival amplitude. Note the considerable
difference in S/N between data from the two sources. The time-axis length is 20 seconds.
82nd EAGE Conference & Exhibition 2020
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Comparison of model building results
The data from both the new source and the conventional source array were used in a model building
exercise with a suite of FWI algorithms. In a manner analogous to the synthetic experiment described
above, the starting model used was deliberately chosen to be as simple as possible – a velocity gradient
hung from the water bottom. It was known from previous surveys in the area, and obvious from
inspection of the raw data, that the area contains significant high-impedance contrast salt bodies, many
of which are close to the seabed. Thus, starting with such a simplistic velocity model offers a marked
challenge to the inversion algorithm.
In this exercise, offsets up to 30km were utilised and the data was restricted to the first arrivals. Thus,
the FWI algorithms were using only the refracted arrivals to derive the velocity model. Figure 3 (left)
shows a comparison of the data from the new source (in black/white) with the modelled data from the
starting model overlain in green (Figure 4(top)). It is clear that the starting model does not fit the real
data at all – in fact the differences are so large that any FWI algorithm using a least-squares objective
function will suffer from cycle-skipping at all offsets.
Figure 3. Comparison of the fit of the modelled data (derived using FWI) to the data from the new
source. (Left) The modelled data (in green) from the initial starting model (a velocity gradient hung
from the picked water bottom). (Right) The modelled data (in green) from the model derived using both
TT-FWI and LS-FWI.
Therefore, we started the model building exercise using a traveltime based FWI approach (using a
similar objective function to that described in Wang et al., 2018). The approach was run in a multiscale
approach with a starting frequency of 1Hz. Even after an inversion of data with a maximum frequency
of 1.5 Hz the FWI process could be seen to matching the first arrival data well. Figure 3 (right) shows
a match between the modelled data and field data after the FWI has been run with frequencies from 1-
4.5 Hz. The majority of the iterations used the traveltime objective function, however a number of final
iterations with a least-squares objective function were undertaken once it was clear from QC that cycle-
skipping in the fitting had been mostly eliminated. The same FWI process was replicated for both the
data from the new source and the data from the conventional source array.
In Figure 4, we compare the starting model (top) with the model derived using data from the new source
(middle) and the model derived using data from the conventional multi-airgun source array. It can be
seen that in both cases the FWI process has inserted high-velocity (4500 km/s and higher) salt bodies
throughout the section. However, the model derived using the new source data suffers from much less
non-geological variation than that derived using the conventional data. In particular, it can be noted that
in a small section of the model where a (human) interpreted salt body from an independent legacy project
was available, the FWI derived model from the new source has a salt body that matches well the top
and flank of the interpreted body. This is not the case for the model derived from the conventional,
multi-airgun source array.
82nd EAGE Conference & Exhibition 2020
8-11 December 2020, Amsterdam, The Netherlands
Figure 4. Comparison of (top) the initial starting model (a velocity gradient hung from the picked water
bottom); (middle) the model derived using both TT-FWI and LS-FWI and data from the new source
(bottom) the model derived using both TT-FWI and LS-FWI and data from the conventional 5110 cubic
inch airgun array. Marked in black is an interpretation of the top-salt boundary from one of the diapirs
derived during a previous model-building exercise.
Conclusions
We have developed a new source, based on airgun technology, that has been designed to improve the
signal-to-noise ratio in the low frequency range (1.5-5Hz) that is crucial to obtaining well-resolved earth
property models with FWI. In this paper we show that the velocity models derived using data from this
new source show clear improvements over those derived in a similar manner from data acquired with a
conventional source array.
Acknowledgements
The authors wish to thank BHP for permission to publish the data and ION for permission to publish
this paper. We also thank Ian Jones for his useful comments.
References
Alkhalifah, T., Sun, B.B. and Wu, Z., 2018. Full model wavenumber inversion: Identifying sources of information for the
elusive middle model wavenumbers. Geophysics, 83, R597-R610.
Billette. F.J. and Brandsberg-Dahl, S., 2005. The 2004 BP velocity benchmark. 67th Conference and Exhibition, EAGE,
Extended Abstracts, B035.
Brenders, A., Dellinger, J., Kanu, C., Li, Q. and Michell, S., 2018. The Wolfspar® field trial: Results from a low-frequency
seismic survey designed for FWI. SEG Technical Program Expanded Abstracts.
Brittan, J., Farmer, P., Brookes, D., Bernitsas, N. and Dudley, T., 2019. Enhanced low frequency signal to noise
characteristics of an airgun technology bases source. SEG Annual Meeting Workshop – New Technologies in Marine
Acquisition.
Brittan, J. and Jones, I.F., 2019. FWI evolution – from a monolith to a toolkit. The Leading Edge, 38, 179-184.
Wang, C., Farmer, P., Yingst, D., Jones, I., Martin, G. and Leveille, J., 2018. Traveltime based reflection full waveform
inversion. 80th Conference and Exhibition, EAGE Extended Abstracts.