Quantitative interpretation using facies based seismic inversion Ehsan Zabihi Naeini*, Ikon Science & Russell Exley, Summit Exploration & Production Ltd

Summary

Quantitative interpretation (QI) is an important part of

successful Central North Sea exploration, appraisal and

development activities. Accurate determination of

hydrocarbon facies is particularly vital as the oil and gas

industry currently faces low oil prices and fewer subsurface

opportunities. This paper presents an integrated workflow

on a recent North Sea discovery using broadband seismic

data and a new joint-impedance and facies based inversion.

The focus was, in particular, on analyzing the best and

worst case scenarios for the distribution of facies to help

optimize future appraisal and development decisions.

Introduction

De-risking, via QI, is an essential part of successful Central

North Sea exploration and appraisal where new discoveries

tend to be close to economic limits. This is currently

particularly important as the oil and gas industry faces a

prolonged period of low oil prices with a subsequent

decline in exploration activities. Additionally, the North

Sea is a mature basin with numerous undeveloped

discoveries which could be economically viable if key

uncertainties are reduced. To do so, it is essential to use the

latest, state-of-the-art technologies. An example is shown

here utilizing a broadband long offset seismic dataset,

broadband well tie estimation, followed by a newly

developed facies based seismic inversion.

The case study shown in this paper centers on a Paleocene

discovery, known as Avalon, in block 21/6b of the UK

Central North Sea located at the north-western edge of the

Central Graben just south of the Buchan Field. The

discovery was initially made using conventional

simultaneous pre-stack inversion followed by a discovery

well that successfully drilled an 85 ft column of oil in good

quality sands. The reservoir sands lie within the proximal

part of the prolific northwest to southeast late Paleocene

Forties and Cromarty depositional trend. This fairway

includes the giant Forties Field.

Locally, Cromarty sands directly overlie and down-cut into

the underlying Forties sands and Lower Sele shales along

the Dornoch shelf edge. The Balder and Upper Sele shale

intervals typically act as the regional seal to Cromarty and

Forties hydrocarbon accumulations.

Generally, Cromarty and Forties reservoirs have high

porosities, high net-to-gross and a high degree of lateral

and vertical connectivity. As a result these sand fairways

act as important conduits for the lateral migration of

hydrocarbons and make these reservoirs particularly

suitable for AVO based inversion techniques.

Method

This paper demonstrates a workflow using a novel facies

based Bayesian seismic inversion technique to analyze the

distribution of reservoir bodies through a range of facies

based sensitivities. Facies based seismic inversion was

introduced by Kemper and Gunning (2014) in which the

low frequency model is a product of the inversion process

itself, constrained by per-facies input trends, the resultant

facies distribution and the match to the seismic. So the

inversion benefits from a rock physics model (and therefore

a low frequency model) per-facies to optimize the

inversion. This new Bayesian inversion system

simultaneously inverts for facies and elastic properties.

In this study the input seismic consisted of conventionally

acquired but broadband processed data with two important

processing steps as follows. Firstly, a pre-imaging de-

ghosting technique, for broadening the bandwidth of the

conventionally acquired towed streamer data, was used to

remove the frequency notches caused by ghost wavelet

interference. Secondly, the processing workflow included a

multi-layer, non-linear, slope tomography to derive the

velocity model for imaging and Kirchhoff pre-stack depth

migration. The advantages of using such broadband seismic

data have previously been demonstrated in the literature

(e.g. Zabihi Naeini et al., 2015) providing increases in both

the low and high frequency signal thereby enhancing

resolution. The presence of seismic signal at low

frequencies however is more important in the context of

seismic inversion as it specifically helps reduce the

dependency on the initial low frequency information.

QI workflows often consist of rock physics analysis, fluid

substitution, synthetic modeling, followed by well tying

and subsequent inversion to elastic properties and facies.

Zabihi Naeini et al. (2016a) demonstrated an example of

the importance of an accurate well tie (and therefore

accurate wavelet estimation) for inversion, specifically

when using broadband seismic data. They concluded that

one has to use broadband wavelets when inverting

broadband seismic to fully benefit from the broad signal

bandwidth. The problem of wavelet estimation for

broadband seismic data, however, arises during the well tie

process when the length (in time) of the well-logs is often

seriously inadequate to provide sufficient constraints on the

low frequency content of the resulting wavelet. Zabihi

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Quantitative interpretation using facies based seismic inversion

Naeini et al. (2016b) discussed this problem in detail and

proposed three different solutions to overcome this issue. In

this study one of their proposed wavelet estimation

techniques was implemented, namely the “parametric

constant phase” method to tie the seismic to the well and

consequently use the wavelet for inversion.

North Sea Case Study

Figure 1 shows the well tie panel and the estimated wavelet

for the mid-angle stack. The constant phase assumption

helps reduce the degrees of freedom for wavelet estimation

and results in a more stable wavelet for short log

sequences. One can also observe reasonable low frequency

decay on the amplitude spectrum obtained inherently as

part of this technique by using multi-taper spectral

smoothing and averaging over many traces around the well.

A good quality well tie can be observed with a cross-

correlation coefficient of 0.78 and a phase error of

approximately 10 degrees. Similar quality well ties were

also achieved for the other angle stacks.

Initial rock physics and forward modelling studies revealed

the Avalon discovery to exhibit a “text-book” Class 3 AVO

(Rutherford and Williams, 1989) anomaly from the top

reservoir reflector. Figure 2 shows the RMS amplitude map

from around the Avalon discovery for both the near and far

partial angle stacks. The main reservoir anomaly is evident

around Well 2.

The first and most critical step for the joint impedance and

facies based inversion technique was to derive impedance

depth trends for each facies. From these per-facies depth

trends equivalent low frequency models are generated, an

essential input to the algorithm. The depth trends are shown

in Figure 3 where five facies are classified: Overburden

hard shale, overburden soft shale, intra-reservoir shale, oil

sand and brine sand. The presence of soft shale can also be

observed in Figure 1 just above the reservoir. Separating

the various shales into different facies types was a critical

factor to improve the inversion accuracy.

Subsequent to running the inversion to derive facies and

elastic properties, QC was performed. Figure 4 shows a

resulting facies section on an arbitrary line crossing both

available wells in this study, which shows an optimized

facies match at both wells. After careful QC, the inversion

was run in 3D with optimized parameters.

A key input of the inversion to facies and elastic properties

are the prior facies proportions which were estimated from

the discovery well, but there was of course some

uncertainty in these proportions away from the wells. In

Figure 5 (left) we show the oil sand time thickness maps

(readily constructed by summing the oil sand facies

samples over the inversion window) for two end member

scenarios, to investigate the sensitivity of the prior facies

proportions. Also, one could further analyze the overall

connectivity of the oil-sand facies and potential satellite

anomalies in 3D (Figure 5, right).

Figure 1: Panels of petrophysical and elastic properties including the brine (blue), oil (green) and gas (red) saturated cases from the

discovery well (Well 2). Petrophysically derived facies before and after up-scaling are also shown in 6th and 7th panel which were used to QC

the inverted facies. Well tie panel is the last panel along with the estimated wavelet for the mid-angle stack.

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Quantitative interpretation using facies based seismic inversion

The final optimized inversion results (prior oil sand

proportion of 3%) demonstrated a very accurate correlation

between measured (in the wells) and modelled (from the

seismic inversion) acoustic & elastic impedances and

resulting facies (Figure 4). The inversion facies output

provided a good result not only matching the oil column

thickness but also the brine filled sands and shales as

encountered in the calibration wells. The inversion also

successfully delineated a thin shale layer below the oil

column observed in the well (previously unobservable

using conventional simultaneous inversion) that had

significant impact on the understanding of potential water

drive during production. Additionally, the output of this

novel inversion technique provided the ideal framework to

quickly and efficiently generate static and dynamic

reservoir models with the facies based output being very

similar to a geo-cellular format. Also, of key importance

was that the facies output was generated without the need

for qualitative and potentially biased interpretation of

conventional impedance products.

Conclusions

Facies based seismic inversion has been demonstrated, via

a North Sea working case study, to provide significant

advantages over more conventional impedance inversion

techniques. When facies based inversion is combined with

broadband data and appropriate broadband well tie

techniques the resulting classified facies output provides a

result ideally suited for geological interpretation and the

generation of static and dynamic

reservoir models. The joint

impedance facies inversion

technique successfully:

Provides a better facies

correlation with calibration

wells.

Inverts for an optimum low

frequency model – thereby

removing one of the most

significant sources of error in

more conventional simultaneous

inversion techniques, where a

low frequency model is an

input, not an output.

Reduces interpretation burden

by producing facies based

output akin to a geo-cellular

model.

Allows a full range of

potential sensitivities to be

explored (Figure 5) therefore

exploring the implications of

inversion error.

Figure 2: Reservoir RMS amplitude maps on near and far angle stacks.

Figure 3: Depth trends for each facies.

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Quantitative interpretation using facies based seismic inversion

Acknowledgements

The authors would like to thank Friso Brouwer, Kester

Waters, Denis Alexeenko, Michael Kemper, Richard Saxby

and Andrew Howard for their contributions. Enquest Plc,

Summit’s partners in the 21/6b Block, are also thanked for

their technical input and permission to publish. Finally,

CGG are thanked for permission to publish results

generated from their CornerStone seismic dataset and in

particular Steve Bowman. Summit Exploration &

Production Ltd is a wholly owned subsidiary of Sumitomo

Corporation, Japan.

Figure 4: Inverted facies section shows a good match at wells (prior oil sand proportion is 3%).

Figure 5: Left figures show the hydrocarbon time thickness map (in ms) and the right figures show the oil-sand facies in 3D obtained using

facies based inversion in two different scenarios.

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EDITED REFERENCES Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2016

SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web.

REFERENCES Kemper, M., and J. Gunning, 2014, Joint impedance and facies inversion — Seismic inversion redefined:

First Break, 32, 89–95. Rutherford, S. R., and R. H. Williams, 1989, Amplitude-versus-offset variations in gas sands:

Geophysics, 54, 680–688, http://dx.doi.org/10.1190/1.1442696. Zabihi Naeini, E., N. Huntbatch, A. Kielius, B. Hannam, and G. Williams, 2015, Mind the gap —

Broadband seismic helps to fill the low frequency deficiency: 77th Annual International Conference and Exhibition, EAGE, Extended Abstracts, 25823.

Zabihi Naeini, E., M. Sams, and K. Waters, 2016a, The impact of broadband wavelets on thin bed reservoir characterisation: 78th International Conference and Exhibition, EAGE, Extended Abstracts, WS01 B02.

Zabihi Naeini, E., J. Gunning, R. White, and P. Spaans, 2016b, Wavelet estimation for broadband seismic data, 78thInternational Conference and Exhibition, EAGE, Extended Abstracts, Tu SRS3 06.

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