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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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