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Data Processing

1 Imperial College London.2 Ikon Science.* Corresponding author, E-mail: [email protected]

Spectral decomposition based inversion: application on Brenda Field, Central North Sea Basin

Baskoro Arif Kurniawan1, Ehsan Zabihi Naeini2* and Dinara Kazyyeva1 show how spectral decomposition based inversion on the Brenda Field can be used to get an idea of how the reservoir varies laterally and also to predict the decompositional environment of the reservoir and give a better understanding of the spatial continuity of the sand reservoir using time-frequency decomposition.

B renda field is located at the Outer Moray Firth in the UK Central North Sea Basin. It lies approximately 120 km northeast of Aberdeen in Block 15/25b as shown in Figure 1. Brenda is a complex stratigraphy

trap from channelized deep-marine turbidite sandstones. The main reservoir interest is an oil reservoir from the Paleocene Forties Sandstone. This sand has the character of a blocky sand package and is located below a consistent package of shale in the top Sele formation, which can easily be corre-lated across the Brenda field.

Structural and stratigraphic correlation has been conducted to get an idea of how the reservoir varies laterally and also to

predict the depositional environment of the reservoir. The struc-tural correlation is illustrated in Figure 2, whereas stratigraphic correlation is illustrated in Figure 3. There are six wells used in the well correlation process along line A-A’ on the location map shown in both figures. Stratigraphic correlation gives interesting information which indicates a geological channel feature when flattening on the top Sele shale marker and also shows lateral sand body continuity inside the channel. These channel features consist of Paleocene sand in the upper part and a shale-filled channel in the lower part. The flattened seismic section on the top Sele shale horizon also enables us to see the channel feature around 2-second TWT, as shown in Figure 3.

Figure 1 Location map showing position of Brenda Field and structural setting of UK central North Sea Basin.

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first break volume 31, October 2013

Figure 2 Structural correlations between well and seismic.

Figure 3 Stratigraphic correlations between well and seismic.

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the impedance contrast at top and base of the layer whose time thickness is the tuning interval 1/2F, and whose base is at travel time t because a tuning response cannot occur before the arrival from the base of the layer. This suggests that an impedance trace can be modelled as a superposition of tuning-thickness blocks for different frequencies. Spectral inversion, as schematically shown in Figure 4, is a trace-by-trace operation consisting of three basic steps:1. Perform spectral decomposition to decompose a seismic

trace into a frequency band component.2. Acquire scaling factor for each frequency components, to

match an earth impedance spectrum.3. Create an output impedance trace by adding tuning

thickness blocks from each frequency component in accordance to the scale amplitudes of the component trace samples.

Spectral inversion on Brenda FieldWe used the near and far angle stacks for this study after applying some data conditioning to further enhance the quality of the seismic data. The key stages of seismic data conditioning were offset spectral balancing, time alignment to remove the residual NMO, and dip-steered denoising. Although all these data conditioning methods played an important role in the overall outcome of this work, the impact of dip-steered filtering was dominant. An example time slice is shown in Figure 5 where we can observe a clearer image with sharper geological features.

The goal of spectral inversion (or any inversion in general) is to derive elastic properties from the seismic data.

We performed spectral analysis in order to get a better understanding of the spatial continuity of the sand reservoir in the Brenda Field. Time-frequency decomposition is a relatively new approach in seismic data processing and interpretation, which has been determined to be valuable in analyzing the time and frequency properties of the seismic signal. Miao and Cheadle (1998) claimed that the visualiza-tion of certain seismic attributes enables us to obtain more detailed subsurface information. Different techniques for spectral decomposition have been developed and described by various authors e.g. Chakraborty and Okaya (1995), Castagna et al. (2003), and Castagna et al. (2004), Wang (2007). We use discrete wavelet transformation to decom-pose the seismic data into different frequency bands for the purpose of spectral and coloured inversion as described in more detail below.

Methodology and workflow

Spectral inversionThe spectral inversion algorithm (Ikon Science proprietary technology) is based on a generic observation that the seismic response at a given trace position and at any sample is the sum of tuning responses at that particular sample. We also know that a seismic tuning response is always stronger than a non-tuned reflection. We can therefore assume that at a given trace position, for any travel time t the signal returned is made up of tuning responses from the layers of different thickness which ends at t. Consider a single frequency F; the signal strength at time t and frequency F depends on

Figure 4 Schematic diagram of the spectral inversion algorithm.

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straightforward. Figure 7 shows an example line of near and far angle stacks and also the resulting Rp and Rs.

Spectral inversion produces a relative impedance estimate or band-limited inversion as an output. This can then be converted to absolute impedance volumes by adding a suit-able low-frequency background model. In this work we only focus on relative impedance and in fact we found it easier to interpret when analysing relative impedance compared to the absolute values. Neep (2007) also states that relative acoustic impedance (AI) data is beneficial in correlating with well logs and geological layering. Relative AI and SI are shown in

The acoustic impedance and shear impedance elastic proper-ties show a good separation of sand from shale as shown in Figure 6. It can also be observed that the sand reservoir here has high relative AI and SI. In this study we only invert for AI and SI and hence the corresponding well logs will be used in the spectral inversion process to derive an appropriate scale for each frequency band.

To accurately invert for AI and SI from near and far-angle stack seismic data, we first calculate the P-wave reflectivity (Rp) and S-wave reflectivity (Rs) using the Fatti second term equation. Inversion to AI and SI from Rp and Rs is then

Figure 5 Time slice through the near-angle stack seismic data before and after dip-steered filtering.

Figure 6 Cross-plots of acoustic impedance vs gamma ray (left) and acoustic impedance vs shear impedance (right). Note yellow is sand, green is shale.

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logs were obtained by band pass filtering the original logs to the bandwidth of seismic.

It can be observed that spectral inversion gives a good correlation with relative AI and SI logs, which can predict a high impedance sand response (note that the mismatch of the first well in the Balder layer is because of large spikes in the logs which cause a big excursion after band pass filtering and cannot be matched by inversion. This can be improved

Figure 8 in which we can observe a good lateral extension of the sand reservoir away from the wells. The sand reservoir is highlighted in red corresponding to high impedance sand, located between top Res and base Res well markers.

Inversion QC analysis of spectral inversion was done by extracting a single trace from the results of relative AI and relative SI on each wells and then compared with relative AI and SI well logs as illustrated in Figure 9. Relative AI and SI

Figure 7 P wave and S wave reflectivity results from near and far angle stack data.

Figure 8 Relative AI and SI from spectral inversion.

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Figure 9 QC analysis of relative AI (top) and SI (bot-tom) from spectral inversion.

Figures 10 Relative impedance extraction with full bandwidth from relative acoustic impedance and relative shear impedance.

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ture. Features that tune at higher frequencies are interpreted to correspond to the thinner flank of the channel highlighted in red, whereas the thickest part of the channel appears in blue. Relative impedance spectral inversion for different frequency bands is able to image the small channel feature (highlighted with a red dash line) inside the main channel. The small channel feature was resolved better at the middle frequency band at 25 Hz. This small channel is not visible on full-band relative AI and relative SI spectral inversion. Being able to identify this small channel feature within the larger main channel system gives valuable information to determine the optimum position for the well development plan.

Spectral based coloured inversionColoured inversion (CI) has been considered as a straight-forward and cost effective technique to invert seismic data into impedance values. It was introduced by Lancaster and

by some log editing procedures in the future). In order to understand the lateral distribution of the sand reservoir in the Brenda Field, relative impedance maps from full bandwidth spectral inversion are shown in Figure 10, where we can see that the channel is nicely imaged. This channel feature has an orientation from SE-NW. High impedance sand reservoirs here are displayed as bright anomalies (in red) on both relative AI and relative SI maps.

An interesting benefit from spectral inversion is the deliv-ery of the inversion result for each frequency band as it uses spectral decomposition to obtain a local frequency spectrum. Figure 11 shows a comparison in three-dimensional views between full-band relative AI and SI spectral inversions and relative AI and SI spectral inversion from individual frequency bands combined in the RGB blending style display.

Each frequency band component responds uniquely to variation in reservoir thickness within the main channel fea-

Figures 11 Comparisons between full bandwidth relative AI and SI with RGB maps obtained from the inversion of each frequency band.

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is recommended (and is part of any quantitative interpretation workflow) to apply some sort of denoising, usually frequency slice filtering or dip-steered filtering, before the application of coloured inversion (see Zabihi Naeini and Morris, 2012).

Coloured inversion is traditionally applied on the whole frequency bandwidth dataset to generate acoustic or elastic properties. The idea of our proposed workflow is simple: we first decompose the seismic data to different frequency subsets, then each subset is denoised according to its frequency and finally we perform CI on each subset. Careful constraints on each frequency band allow us to control the presence of the undesired energy on each band and consequently enhance the output quality. Another benefit is that the denoising operator is adjusted based on the frequency content i.e. lower frequen-cies are usually over-sampled and hence a bigger spatial operator can be used (Zabihi Naeini and Morris, 2012). The results of CI on each band are finally merged together in order to compare with the output of the application on the full initial bandwidth. Figure 12 shows a schematic diagram of the proposed workflow in comparison with the traditional approach. A similar approach was also adopted by Helmore (2009) for denoising data more effectively.

Spectral based coloured inversion on Brenda FieldIn this paper, we only colour invert the near angle stack volume to derive the acoustic impedance. Extension to any other elastic properties is straightforward but not shown in

Whitcombe (2000) to invert seismic data to relative impedance by deriving a convolutional operator that shapes the average seismic spectrum to a representation of that observed in the impedance well log (acoustic or elastic). This operation usually involves boosting the low frequency part of the spectrum as the earth spectrum is richer in the low frequency range. Therefore it

Figure 12 Traditional (left) versus new approach (right) for coloured inversion workflow.

Figure 13 Different frequency bands of near-angle stack seismic along with their coloured inversion operator.

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this paper for the sake of simplicity. Seismic data is separated into low, medium and high frequency subsets by using the discrete wavelet transform (DWT) decomposition. We then denoise each subset according to its central frequency as mentioned in the previous section and derive the resultant CI operator. This is shown in Figure 13.

Figure 14 demonstrates relative AI volumes derived from the traditional CI and our proposed workflow. It can be observed that the new workflow has enhanced the continuity of the events (for example, see the event at 2.2–2.3 seconds). It also exhibits a better correlation with the well log as overlaid on the seismic section. Furthermore, it has a higher resolution and therefore results in more identifiable geological bounda-ries. The impact on the resolution is also clear when looking at the frequency spectrum as shown in Figure 15 where it can be seen that the proposed workflow results in a broader bandwidth. Figure 16 shows a map view of the two workflows where there is a substantial improvement on the boundaries of the sand channel from the result of the proposed workflow. These sandstone distribution patterns correlate with what we observed from the spectral inversion approach and also with the local geology and well logs.

ConclusionsWe showed the benefits of using spectral information in the context of spectral inversion and coloured inversion. Both proposed methodologies clearly delineated the channel fea-ture and matched with the well log observations. For spectral inversion, we proposed to extract different frequency bands of the impedance volumes and blend the results in an RGB manner. The outcome can then be related to the thickness in the reservoir and help to extract other detailed geological features which cannot be seen on full-bandwidth style inver-sion. Also a new spectral decomposition-based workflow was proposed for coloured inversion. This enabled us to effectively reduce the impact of noise and better stabilise the shaping operator which then resulted in a better impedance volume with higher resolution and sharper boundaries.

Figure 14 Relative AI from traditional and the proposed workflows for col-oured inversion.

Figures 15 Amplitude spectrum of relative AI from traditional and the pro-posed workflows for coloured inversion.

Figures 16 Map view of relative AI from tradi-tional and the proposed workflows for coloured inversion.

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Miao, X. and Cheadle, S. [1998] Noise attenuation with Wavelet

transforms. 68th SEG Annual International Meeting, Expanded

Abstracts, 1072–1075.

Moser, J., Strijbos, F. and Castagna, J. [2004] Spectral variance in

comparison with conventional spectral decomposition attributes.

74th SEG Annual International Meeting, Expanded Abstracts,

441–444.

Neep, J.P. [2007] Time variant coloured inversion and spectral blueing.

69th EAGE Conference and Exhibition, Extended Abstracts.

Partyka, G.A., Gridley, J. and Lopez, J. [1999] Interpretational aspects

of spectral decomposition in reservoir characterization. The

Leading Edge, 18, 353–360.

Portniaguine, O. and J.P. Castagna [2004] Inverse spectral decomposi-

tion. 74th SEG Annual International Meeting, Expanded Abstracts,

1786–1789.

Richard, P.C., Lott, G.K., Johnson, H., Knox, R.W.O’B. and Riding,

J.B. [1993] Lithostratigraphic nomenclature of the UK North Sea.

Published by the British Geological Survey on behalf of the UK

Offshore Operators Association.

Wang, Y. [2007] Seismic time-frequency spectral decomposition by

matching pursuit. Geophysics, 72(1), V13–V20.

Zabihi Naeini, E. and Morris, H. [2012] Novel noise reduction for

time-lapse and AVO workflow. First Break, 30(9), 67–76.

AcknowledgmentsWe would like to thank Ikon Science for permission to pub-lish this paper. We also thank David Gawith and Mike Bacon for useful discussion during the course of this work.

ReferencesCastagna, J., Sun, S. and Siegfried, R.W. [2003] Instantaneous spectral

analysis: Detection of low-frequency shadows associated with hydro-

carbons. The Leading Edge, 22, 120–127.

Chakraborty, A. and Okaya, D. [1995] Frequency-time decomposition of

seismic data using wavelet based methods. Geophysics, 60, 1906–1916.

Fatti, J.L., Smith, G.C., Vail, P.J., Strauss, P.J. and Levitt, P.R. [1994]

Detection of gas in sandstone reservoirs using AVO analysis: A 3-D

seismic case history using the Geostack technique. Geophysics, 59,

1362–1376.

Helmore, S. [2009] Dealing with the noise – Improving seismic whitening

and inversion workflows using frequency split structurally oriented

filters. 79th SEG Annual International Meeting, Expanded Abstracts,

3367–3371.

Lancaster, S. and Whitcombe, D. [2000] Fast-track ‘coloured’ inversion. 70th

SEG Annual International Meeting, Expanded Abstracts, 1572–1575.

Marfurt, K. J. and Kirlin, R. [2001] Narrow-band spectral analysis and

thin-bed tuning. Geophysics, 66, 1274–1283.

Naturally Fractured Reservoirs in Real Life

This workshop will cover fi ve key topics through technical and keynote presentations and a dedicated poster session. The topics under discussion are:• Surface Outcrop Analogues• Geomechanics• Characterisation• Modeling• Integration

Field Trip to Salakh ArcSunday 8 December 2013This one day trip organised in association with the Geological Society of Oman will discuss fracture sets in terms of their characterisation and relationship to each other and to stratigraphic units. The locations will provide the opportunity to look at fractures both across the stratigraphy and on bedding planes, allowing discussion on how fractures may be seen in the limited well sections and how modeling is challenged to provide a realistic model from limited data. Limited places available so register early.

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