3D-3C Multicomponent Seismic A successful fracture ...
Transcript of 3D-3C Multicomponent Seismic A successful fracture ...
technical article first break volume 34, January 2016
3D-3C Multicomponent Seismic – A successful fracture characterization case study in Algeria
M. Donati3, J.L. Piazza5*, A. Rollet1, S. Baillon1, D. Marin1, V. Belz1, H. Toubiana1, J. Castro2, A. Bouheouira2, T. Belhouchet2 and M. Raha4
Abstract Multi-component acquisition has been used in the industry for many years for fracture density and orientation studies with
proven success. In the field under study, it has been observed from well information at target level that there is a relation -
ship between gas production and natural fractures, faults, and changes in facies. The target horizon located at a depth of
approximately 1950 m is composed of low-porosity gas-bearing Ordovician sandstones of 5 to 6% average porosity. The
Ordovician is characterized by a highly heterogeneous matrix affected by natural fractures.
This paper describes a 3D multi-component land seismic project conducted in Algeria, designed to perform a fracture char-
acterization study. This pilot survey, acquired in 2010 over the Tin Fouyé Tabankort West block, is the first land 3D -3C
acquisition ever performed in Algeria. It covers an area of about 67 km2 centered on a well location. The following project
phases will be described hereafter: (1) 3D-3C survey design and field operations, (2) Main steps of the PP and PS-wave
pre-processing sequences, (3) PP and PS-wave interpretation study at deep levels made possible by the cleaned and signal-
preserved seismic data.
The resulting PP seismic dataset from the 3D-3C survey provides a better resolution of subtle faults and lineaments than the
conventional 3D acquired in 2003 and reprocessed in 2010, even though the noise level remains slightly high. Furthermore,
PS seismic data shows evidence of azimuthal anisotropy in relation to the faults and lineaments interpreted on the PP dataset .
Anisotropy analysis results tie reasonably well with FMI information available in the Ordovician, showing great potential
for using this technology for fracture studies.
Introduction
The 3D-3C seismic survey acquired in the Tin Fouyé
Tabankort West (TFT-West) area in Algeria aims to charac-
terize the fractures to evaluate their influence on gas produc-
tion. The main purpose is to gain a better understanding
of the relationship between fractures, faults, structure, and
changes in facies and their impact on the gas production in
the area.
The variations in gas production over the field may
be related to lateral changes in the fracture orientation
and density. This has been evidenced by the fact that some
wells with low/poor density of open natural fractures are
nevertheless good producers whereas others with the same
fracture characteristics are poor producers. We observed the
same situation in wells that have a regular to good density of
natural fractures, where the gas production is poor in some
wells and good in others. In both situations, in some wells
it was necessary to induce fractures to obtain flow and gas
production. The fracture orientation observed in various FMI
acquired in different wells in TFT-West associated with the
1 CGG.
2 GTFT.
3 Repsol.
4 Sonatrach.
5 Total.
* Corresponding author, E-mail: [email protected]
top of the Ordovician shows lateral changes in the fracture
orientation; many of these changes could be associated with
faults. With the analysis of the 3D-3C seismic data, we seek to
gain a better understanding of the driving mechanism of gas
production and the relationship between faults and fractures.
We will show that with a customized processing work-
flow it is possible to remove noise and obtain final images
as good as if not better, than those obtained with a more
conventional PP dataset. Fracture distribution characteriza-
tion results from PP and PS data are encouraging for future
use of multi-component technology in Algeria to study
fracture distributions and their impact on gas production.
3D-3C seismic provides an improvement in the lateral and
vertical resolution in fault definition. Well-tied fracture
orientation and anisotropy maps could be used for exploita-
tion decisions.
Geological context The reservoirs are composed of a highly heterogeneous
matrix with medium-to-low porosity Ordovician Unit IV
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technical article first break volume 34, January 2016
sandstone overlaid by Silurian shale (cap rock). This gas
field is characterized by the presence of a natural fracture
network. The reservoir facies correspond to the association
of two distinct sedimentological elements, the ‘en cordons’
sandstones showing channelling features and the ‘megaridge’
sandstones related to turbiditic lobes. These facies, interca-
lated with shales, were deposited in a marine platform envi-
ronment. The average porosity of the sandstone reservoirs is
of the order of 5 to 6%.
The main horizons interpreted on the seismic datasets
(Figure 1a) are: (1) the Hercynian Unconformity which
displays large-scale truncations of the Carboniferous series,
(2) The Frasnian unconformity with its outstanding high
frequency and continuous character, (3) The Silurian uncon-
formity located at the top of the Silurian shale interval, (4)
The Top Ordovician which corresponds to the strong imped-
ance contrast between the Silurian shale and the Ordovician
sandstone reservoirs and (5) The Top Basement. Figure 1b
presents a detailed stratigraphic/sedimentology summary of
the target zone.
Among all types of fractures, the tectonic fractures are
thought to potentially play a major role in the dynamic
behaviour of the field due to their lateral extent and
absence of cementation. They are often distributed in
fracture corridors sometimes related to faults or to ‘en
echelon’ systems.
Four sets of fractures oriented NW-SE, NE-SW, N-S, and
E-W are identified in the wells. These directions correspond
Figure 1a Illizi Basin stratigraphic column.
to successive compressional and extensional tectonic phases
recorded in the area. Fracture corridors, typically less than
20 m in width, are observed in some wells of TFT-West.
A study based on imaging and sonic logs shows that the
maximum horizontal stress is globally oriented in the N140°
direction even though local variations may exist. The regional
fracture model suggests that the fractures are mainly related
to faults but well data also suggest that the development of
fractures may also be controlled by the lithology specifically
in the TFT-West area.
3D-3C acquisition survey
The pilot survey was specifically designed to obtain a regu-
lar offset-azimuth distribution for the fracture study. The
3D-3C seismic survey acquired between December 2010
and March 2011 covered 67 km2 with a receiver/shot cross-
spread layout as shown in Figure 2a: (1) 16 receiver lines ori-
ented approximately N-S, a 15-m receiver interval, a 210-m
receiver line distance and 214 receivers per line, (2) 40 source
lines oriented W-E, a 30-m shot interval, a 210-m shot line
distance and 274 shots per line, and (3) 3C single MEMS
(micro-electro mechanical system) sensors laid out and kept
at the same location during the recording of 10,960 shots.
The inline component of the 3C geophones was oriented
toward the south.
This geometry provides a good sampling of the pre-stack
data, a regular offset -azimuth distribution that is ideal
for performing a fracture study (Figure 2b), an increased
PS migration area allowing data processing in the com-
mon offset vector (COV) domain, along with a reduced
environmental impact, and an optimized field production.
The recording parameters are: Sweep Length: 8 s, Listening
Time: 6 s, Linear Sweep 7-70 Hz, Low-Cut Filter: 3 Hz/12 dB/
octave, High-Cut Filter: 80% Nyquist frequency without any
notch/noise elimination filter applied.
Figure 1b Detailed stratigraphy picture of the target zone – Top Ordovician.
Formation de l’Oued Imirhou
Slu
rien
800 m
Formation de Tamadjert
Formation d’In Tahouite III-3
Formation des Ajjers
III-2
III-1
Cam
bro
-Ord
ovi
den
II
0
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Figure 2 (a) 3D-3C Pilot survey layout and source geometry, and (b) Upper section fold map for PS data (all offsets versus 0-2875 m offset), Lower section azimuth
versus offset distribution.
technical article first break volume 34, January 2016
PS FOLD MAP - ALL OFFSETS PS FOLD MAP – OFFSET RANGE: 0 – 2875 m
ROSE DIAGRAM AZIMUTH DISTRIBUTION vs. # OF TRACES
SOURCES RECEIVERS
SHOOTING PATTERN
Acquisition parameter estimation and field operations
The acquisition parameters were estimated through a resolu-
tion analysis using well data, for a maximum 10° geological
dip and a maximum frequency content of 50 Hz. Figures
3, 4, and 5 show: (1) Interval velocity changes in reservoir
(Figure 3b), (2) Bin size ≤ 30 m for proper target sampling
and lateral resolution (Figures 3a and 4a), (3) Raw shot anal-
ysis showing that a bin size of 15 m is required to adequately
remove the ground roll, (4) Migration aperture of 1250 m,
and maximum offset of 2500 m (Figure 5), (5) Vertical reso-
lution of the order of 40 m. Layers thinner than 20 m can
be detectable but not resolvable. P-wave velocity-to-S-wave
velocity ratio (Vp/Vs) in the target reservoir – estimated from
well data – is close to 1.5. The terrain of the pilot survey is
mainly flat but characterized by a very hard soil which had
an impact in terms of acquisition time, deployment effort,
geophone coupling, polarity, and battery QC. A complete
technical audit was performed for all equipment and a full
set of manufacturers’ tests applied to both recording instru-
ments and line equipment.
During the acquisition an increase in wind velocity
was observed. Wind flows produced considerable noise on
data especially on the vertical component compared to the
horizontal components. High-velocity winds affected data
quality, impacted daily production, and led to a significant
time delay in operations. Wind flow velocity behaviour was
not the same during the entire acquisition, leading us to
reshoot some locations to improve data quality. In Figure 6,
we can observe that the vertical component appears to be
more affected by wind noise than the horizontal components.
The amplitude and noise levels in the vertical component are
higher compared to the horizontal components for the same
shot location and/or acquisition day. RMS amplitude maps
show similar behaviour in the horizontal components with
lower amplitude and noise levels in the cross-line component.
Horizontal components seem to be less sensitive to ambient
noise. A frequency content of 5-50 Hz for the PP data and
Figure 3 (a) Lateral resolution, and (b) average, interval, and RMS velocities.
5-40 Hz for the PS data was observed. Ambient noise is
characterized by frequencies between 3 and 60 Hz.
PP and PS processing Given the three-component acquisition, a critical initial step
in the processing sequence is the control and determination
of sensor verticality and orientation. This was performed
using a simple modelling of the first breaks that gives a 3D
rotation matrix of the estimated sensor in-field orientation
(Maillet and Gratacos, 2008). The method yields quality
control scalars that help in identifying poorly orientated
receivers. In this dataset, the refractor is not as simple as a
single arrival, but consists of shallow marker interferences.
Therefore the reorientation required some care in data pre-
conditioning and defining the analysis window limits in time
and space to achieve reliable results; this work was facilitated
by the use of an interactive workstation. We found 90%
of the receivers to be within +/-5 degrees of their correct
orientation. Prior to the orientation analysis, the horizontal
components of the recorded data were also analyzed to
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technical article first break volume 34, January 2016
Figure 4 (a) Resolution analysis and (b) bin size and vertical resolution. Figure 5 (a) Migration aperture, (b) maximum offset.
ensure that the spectral levels were equivalent and therefore
calibrated correctly.
The P-wave statics were determined by first-break picking
and a tomographic decomposition constrained by uphole
data. This part was not critical, and gave moderate PP static
corrections. The receiver side shear-wave static solution was
calculated by cross-correlation of a shallow PP and PS horizon.
Both P-wave and S-wave statics solutions were then improved
with a surface-consistent residual statics algorithm using a
non-linear scheme based on a Monte-Carlo method coupled to
a simulated annealing approach (Le Meur and Poulain, 2011).
The main processing steps for both PP and PS data
are: (1) Noise removal, (2) Velocity estimation (two itera-
tions), (3) Surface-consistent deconvolution and amplitude
corrections (two iterations), Residual statics corrections
(three iterations), (4) Vertical transverse isotropic (VTI) pre-
stack time migration (anisotropic PSTM), and (5) Residual
move-out (RMO) corrections. For PS processing, a specific
anisotropy analysis study was performed in view of fracture
orientation and density estimation using pre-stack time
migration (PSTM) volumes of the radial and transverse
horizontal components. The final PP and PS PSTMs were
done in the COV domain on a 7.5 m x 7.5 m grid.
Denoising sequence
Land datasets acquired with single sensors are often highly
affected by noise, which usually leads to a final PP prod -
uct with limited quality in comparison with a PP dataset
recorded using a conventional geophone array. With this
project, we demonstrated that achieving good PP and PS
final quality is possible by carefully choosing and tuning a
denoising sequence.
The preferred gather for 3D PP and PS denoising consists
of a cross-spread (one receiver line illuminated by one shot
line that creates a single-fold subsurface volume) which has
the advantage of finely sampling the 3D wavefield at the
expense of shaping the circular noise cone into a sort of
‘rounded square cone’.
The PP and PS denoising workflows based on a cascading
approach include three main processes: (a) Frequency-
dependent noise attenuation, (b) Impulsive noise removal
and (c) Adaptive ground roll attenuation. An additional noise
attenuation step based on coherent noise attenuation (CNA)
and coherent signal estimation (CSE) was applied to the PS.
Common PP and PS denoising steps
The high level of non-linear noise, such as impulsive noise,
random noise, spikes, and bursts requires the application of
different noise attenuation strategies prior to applying the
linear noise attenuation process.
1) Frequency-dependent noise attenuation (Li and Couzens,
2006):
This process attenuates the high-amplitude noise in
decomposed frequency bands, allowing the suppression
of noise specific to different frequency ranges and
different times. It leads to a more robust removal of
linear coherent noise applied later in the sequence.
2) Impulsive noise removal (Soubaras, 1995):
This algorithm is a variant of projective filtering and is
based on data predictability to surgically reconstruct
very noisy traces. The types of noise addressed by this
technique are those related to ambient noise produced
by strong winds or poorly coupled receiver stations. For
the noisier frequencies, the filter estimates the values that
make the denoised trace most predictable with respect to
all the traces in its vicinity.
3) Adaptive Ground-roll attenuation (Le Meur et al., 2008):
This technique iteratively estimates from the data
itself the ground roll phase and group velocities, while
preserving the signal defined by its kinematics (e.g.,
NMO correction in its simplest version). The noise model
is then adapted to the data and removed.
Eventually, some remaining spikes or bursts were removed
by median filters. Even though the final products only had
the steps above applied, an additional FKxKy filter with
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large velocity taper was also applied to facilitate the velocity
picking. The success of this iterative process is confirmed by
the systematic preservation of the signal at each processing
step (Figure 7).
PS denoising strategy
The PS denoising sequence is basically built up from the PP
sequence. However, for the horizontal components, different
types of projection systems can be selected. It is common
practice to rotate first to the radial-transverse system and
denoise only the radial projection. This is generally valid
for waves that are predominantly radial. Alternatively, the
denoising process can be applied independently in the inline,
cross-line (original acquisition) projections. In this configura-
tion, the two components contain similar datasets and allow
us to better preserve the consistency between the two compo-
nents, provided the polarity behaviour is taken into account.
This last approach was favoured over the first one.
As for PP data, the success of the PS denoising sequence
comes from careful preservation of the primary signal at each
processing step taking great care to target only the specific
characteristics of each type (ambient noise, ground roll, coherent
noise with different velocity ranges) of noise (Figure 8).
Additional denoising steps for PS data
In addition to the denoising sequence applied to the PS
data, we found that the data could greatly benefit from an
additional noise attenuation step known as coherent noise
attenuation and coherent signal estimation. This technique
tries to emulate an FK filter, with the major difference that it
is not applied in the frequency/wave-number domain but in
the frequency/space domain. It has the advantage of accom-
modating irregular geometries, controlling the ‘aperture’ (or
size in space) of the operators and better preserving the signal
characteristics such as the AVO. It is an iterative processing
sequence targeting specific and limited ranges of velocity
applied in shot and receiver domains. Four iterations were
applied for the PS data.
Anisotropic pre-stack time migration strategy for both
PP and PS datasets
Pre-stack time migration (PSTM) is an efficient imaging
technique and a routine step in the compressional wave
Figure 6a RMS amplitude profiles versus daily production. From top to bot-
tom: vertical, inline, and cross-line components.
Figure 6b RMS amplitude maps per shot line dis-
tribution for vertical, inline and cross-line compo-
nents during acquisition.
Figure 7 PP initial stack (left) and PP denoised
stack (right).
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technical article first break volume 34, January 2016
Figure 8 PS initial stack (left) and PS denoised
stack (right).
processing flow. The most important challenge of converted
wave pre-stack time migration is to find the best P - and
S-wave velocity fields to compute proper travel times.
The Kirchhoff algorithm for both PP and PS data is a
trace-by-trace migration, which treats each output sample as
the apex of a diffraction curve. Input samples are summed
along the diffraction curve, which is characterized by a
locally defined RMS velocity. The reflector image is thus built
by constructive interferences. Implementation is designed to
preserve with very good accuracy the amplitude and phase
of the input signal, making it well-suited for post-migration
AVO analysis. The use of a model-based weighting scheme
partially compensates the amplitude artefacts introduced by
irregularities in the acquisition geometry.
The ray-bending anelliptic algorithm based on shifted
hyperbola move-out was used to correct for layering and
any anisotropy effects. This parameterization for non-
hyperbolic move-out delivers correct time for large dips up
to 50 degrees. Shifted hyperbolas were derived automatically
from the RMS velocity field (pure 4th order layering term).
Figure 9a Vertical gamma (y0 ) field
(in PP time).
Figure 9b Effective gamma (yeff ) field
(in PS time).
The data was sorted in the COV domain to preserve the
azimuth and offset information during the following process-
ing steps and take advantage of the offset vector tile (OVT)
based migration. Four azimuth sectors were used to compute
the Effective gamma volume.
PS anisotropic Kirchhoff pre-stack time migration
The analytical expression of the travel times for PS-wave
anisotropic pre-stack time migration requires the following
parameters (Li et al., 2004): vertical and effective P and S
velocity ratios, called respectively vertical gamma (y0) and
effective gamma (yeff), PS imaging velocity and PS anisotropic
effective parameters.
The interdependent PS imaging velocity and effective
gamma fields are determined in an iterative approach.
Effective gammas are picked from spatial cross-correlations
between forward and reverse raypaths. The correct value is
obtained when structural events occur at the same place on
both sections, resulting in an image with the best focusing.
Initial registration – Vertical gamma (y0 ) field derivation
The first step of the pre-stack time migration is to derive an
initial 3D vertical gamma field (defined as follows: y0 = 2 Tps/
Tpp - 1, where Tpp and Tps express respectively PP and PS
times of the same event). For this purpose, four horizons –
Unconformity, Frasnian, Silurian, and Top Ordovician – have
been jointly interpreted on both PP and PS pre-stack migrated
volumes to ensure proper restitution of the structures. A 3D
smoothed average y0 field was derived from these horizons.
Focusing analysis – Effective gamma (yeff ) field derivation
Focusing analyses based on spatial cross-correlations between
forward and reverse azimuth sector stacks computed from
OVT migrated gathers were first performed. Four azimuth
sectors defined clockwise from North were selected as follows:
n Azimuth 1: N55° to N105°
n Azimuth 2: N145° to N195°
n Azimuth 3: N235° to N285°
n Azimuth 4: N325° to N15°
A series of focusing analyses were produced for a set of con- stant effective gamma values ranging from 1.4 to 3.4, with
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technical article first break volume 34, January 2016
an increment of 0.2. For a given bin location and a given
time, the gamma value that gives the most symmetrical cross-
correlation is selected to build the effective gamma field. A
geo-statistical filtering was applied to eliminate the anoma-
lous values due to the manual picking that could generate
artefacts during the migration. Final vertical and effective
gamma fields are shown in Figures 9a and 9b respectively.
PS Imaging velocity field With the vertical and effective gamma fields derived, a scan
of pre-stack time migrations was performed using perturba-
tions (from 95% to 105%, every 1%) of the initial velocity
field to build the final PS imaging velocity field.
PS Final migration Gamma and migration velocity fields described above were
derived using the radial component only. However, for the
final migration it was necessary to migrate both radial and
transverse components the same way to ensure that any
shear-wave splitting effects were retained and, in particular,
any polarity reversal of the transverse data was not compro-
mised.
PP and PS pre-stack time migration workflow summary
The pre-stack time migrations were performed using an aper-
ture of 3000 m and a constant dip limit of 30° in the final 7.5 m x 7.5 m bin grid.
Figure 10 Snail gather associated to the PP pre- STM gather. Traces are ordered in offset range
class with increasing offset and within each offset range class according to increasing azimuth. We
can observe the azimuthal variation of the residual move-out characterized by a significant wobbling
effect.
PP pre-stack time migration flow
Pre-processed bin gathers
Initial 3D velocity derivation used as a reference and
initial PSTM
Structural 3D velocity derivation based on initial velocity perturbation scan
3D geo-statistical filtering of the above migration
velocity field
3D ray-bending anelliptic Kirchhoff PSTM for migrated image gather
output
High-density automatic 2nd and 4th order residual move-out (rmo) velocity picking and corrections
High-density azimuthal rmo picking and parabolic
elliptical rmo corrections
High-resolution Radon de-multiple
Final mute adjustment
Full stack
Post-processing
Radial and transverse pre-stack time migration flow
Pre-processed bin gathers
Initial PS imaging velocity derivation and initial PS
PSTM
Vertical Gamma derivation (γ0)
Focusing analyses and γeff derivation
γeff 3D geo-statistical filtering
PS imaging velocity derivation based on initial PS
velocity perturbation scan
3D geo-statistical filtering of the above PS imaging
velocity field
3D ray-bending Kirchhoff PSTM for radial &
transverse migrated image gather output
Birefringence study
High-density automatic 2nd and 4th order residual
move-out
High-resolution Radon de-multiple
Final mute adjustment
Full stack
Post-processing
Table 1 PP and PS pre-stack time migration workflows.
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technical article first break volume 34, January 2016
Azimuthal anisotropy in the multi-component data
Evidence of azimuthal anisotropy in the PP data
The evidence of azimuthal anisotropy on PP data is shown
in Figure 10 in the snail gather display where traces are
ordered in offset range class with increasing offset and within
each offset range class according to increasing azimuth.
On this gather, the residual move-out is characterized by a
significant wobbling effect that can be interpreted as an azi-
muthally varying residual move-out and has been corrected
by a parabolic elliptical residual move-out. In the framework
of this study mainly oriented toward the interpretation of
the PS converted waves, the azimuthal anisotropy of the PP
waves in terms of velocity and Amplitude versus Offset was
not studied.
PS azimuthal anisotropy estimation and compensation
One major difference between PP and PS datasets is the bire-
fringence (shear-wave splitting) phenomenon, whereby a sin-
gle interface gives rise to two orthogonally polarized events
that are separated in time. The estimation and correction for
shear-wave splitting is a critical key step in PS processing. If
not properly handled, this effect degrades the quality of the
processing result, and might confuse some pre-stack steps as
the event polarity depends on the source-receiver azimuth
with respect to PS1 and PS2 directions, the so-called natural
(or anisotropy) directions. The success of this process largely
depends on the efficiency of the denoising sequence, as the
surface wave can easily bias this estimation.
We first briefly review the method used for the estima-
tion of the natural directions. As described by Gratacos
(2006), this estimation is based on the least -squares fit of
the observed data to a 1D HTI model of converted waves.
The proposed algorithm fits the observed data to t rp(t), the
model of earth response for converted waves being defined
as follows:
trp(t) = Rps1 (t) cos (a-flp) cos (a-8p) + Rps2 (t) sin (a-flp) sin (a-
8p)
= Rps (t) cos (8p-flp) + 5Pps (t) cos (2a-8p-flp)
In this model, p is the trace index for the gather under consid-
eration (for n traces in total), a is the fast direction (so a+π/2
is the slow direction), flp is the heading of the geophone that
recorded trp(t), and 8p the source-receiver azimuth (see figure
above). Therefore Rps1 and Rps2 are the fast and slow components
of the PS wavefield, which can also be interpreted in terms of
an average isotropic contribution Rps=1/2(Rps1+Rps2) and an
anisotropic contribution 5Rps=1/2(Rps1-Rps2).
It is important to note that this modelling nowhere
introduces the notion of a delay between the fast and slow
images, nor does it assume they have a common wavelet. We
merely request the two images to be different.
This least-squares technique is robust as it involves only
proper weighted and stacked data, hence reducing the effect
of noise.
The method described above provides the anisotropy
directions (the direction of the fast anisotropy axis PS1,
in degrees clockwise from North). Time delays (in ms)
between PS1 and PS2 (slow anisotropy axis) components are
estimated using cross-correlation after determining the split-
ting directions. In addition, two associated quality control
attributes that allow a quantitative estimation of the amount
of anisotropy present in the dataset are derived:
n Normalized error function (NEF) for which a value of
1 indicates a perfect fit to the model
n Maximal variation of the error function (MVEF) which
is defined as the minimum to maximum range of the
error function over all possible values of the splitting
directions. High values of MVEF imply strong variations
of the error function with respect to azimuth angles.
This attribute confirms the presence of anisotropy and
indicates the amount of anisotropy observed in the data.
It was necessary to spatially filter the estimated directions, as
they were slightly affected by noise. A circular statistical filter
weighted by the MVEF was designed and the time delays
were recomputed.
This analysis was performed on migrated gathers in the
COV domain as the true common conversion point (CCP)
location is accurately defined by the PS pre-stack time migra-
tion algorithm, and the azimuthal information is preserved
by the use of the OVT-based migration. Moreover, migrated
gathers lead to a better signal-to-noise ratio than asymptotic
common conversion point (ACCP) gathers.
The radial and transverse migrated stacks after residual
NMO corrections show the presence of anisotropy in the
Devonian and Ordovician intervals. In order to opti -
mize the anisotropy compensation procedure, a detailed
analysis was performed on five levels: constant time at
600 mstwt, Hercynian unconformity, Frasnian, Silurian
and Top Ordovician. Each level was analysed separately
within a window of 200 ms centered on the corresponding
horizon.
This analysis showed no significant anisotropy at
600 mstwt and progressively increasing time delays from the
Src p azimuth θp
Slow axis α+π/2 Fast axis α
Geophone p
heading βp
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Figure 11 Anisotropy results related to Top Ordovician.
technical article first break volume 34, January 2016
Figure 12 Radial pre-STM stack - Before birefrin-
gence compensation (left), after compensation per
layer stripping (right).
Hercynian unconformity down to Ordovician with consist -
ent anisotropy orientations.
Finally the PS anisotropy compensation was applied
using a layer-stripping approach based on two intervals:
Hercynian unconformity to Top Frasnian (1400 ms PS
time) and Silurian to Top Ordovician (2000 ms PS time).
Anisotropy orientation and time delay maps for both inter-
vals were obtained. Figure 11 shows the results obtained for
Top Ordovician. For each level, we observed a good correla-
tion between splitting time delays and QC attributes, such as
the MVEF, which means that we can be confident about the
estimated orientations.
This study, leading to an improvement in the radial
energy, confirmed what was previously known from wells
over the area. In addition, the fault definition at 2000 ms PS
time was greatly improved (Figure 12).
Figure 13 shows a comparison between initial and final
results for both PP-wave and PS-wave data. After tremen-
dous efforts, high-quality PP-wave and PS-wave datasets
were obtained, with dominant frequency at target level of
around 25 Hz for the P-wave pre-stack migrated data and
15 Hz for PS-wave pre-stack migrated data, respectively.
Interpretation The interpretation study started with a comparative evalu- ation of the 3D-3C PP dataset and the conventional 3D
survey acquired in 2003 over the whole TFT-West area
and reprocessed in 2010 using a fully up-to-date process-
ing sequence. The pre-stack time-migrated seismic cube
extracted from the conventional 3D, referred to hereafter
as the conventional P stack, on the area of the 3D-3C pilot
survey was also integrated into the study as a reference for
comparison purposes.
We first focused on the structural aspects and more
specifically on the characterization of faults and fractures in
the Ordovician reservoir. One well was used to calibrate the
main seismic horizons, namely:
n Hercynian unconformity which is characterized by
strong low-frequency amplitudes and top-laps related to
erosional truncations on both PP and PS wave datasets
n Near Frasnian unconformity marked by a continuous
high-frequency event on the PP-wave dataset but by a
weak and rather discontinuous reflector on the PS dataset
n Top of Ordovician formation which is a really strong
and continuous reflector on both PP and PS-wave
data related to outstanding P- and S-velocity contrasts
between overlying shale and underlying low-porosity
sandstones.
The multi-component seismic data set used in the interpre- tation study includes a PP pre-stack time-migrated stack,
referred to hereafter as the PP stack, a radial stack, referred
© 2016 EAGE www.firstbreak.org 39
technical article
Figure 13 Initial stacks versus final stacks: (a) Initial
radial stack, (b) Final radial pre-STM stack, (c) Initial
PP stack, (d) Final PP pre-STM stack.
first break volume 34, January 2016
Figure 14 Composite time structure (colour) and
coherency (grey) maps at Top Ordovician from
conventional P stack (left panel) and PP stack
(right panel).
Figure 15 Comparison along a selected cross-section extracted from conventional P stack (left) and PP stack (right).
to hereafter as the PS stack, and a transverse PS pre-stack
time migrated stack as well as PP and PS stacking velocity
fields, γ ratio cubes and anisotropy maps extracted at the
Near Frasnian and Top Ordovician levels in the course of the
PS processing phase.
One well with lithology, density, P- and S-sonic, porosity
and Gamma-Ray logs and VSP (P-wave only) data was used
throughout the interpretation study to calibrate the seismic
cubes and estimate the seismic wavelets and the γ ratios
at different vertical scales and intervals in the borehole. A
residual zero-phasing calibrated on the well data was applied
to the PP stack before interpretation.
PP Interpretation
A complete horizon interpretation of the 3D PP and con-
ventional P stacks was first performed. Structural attrib -
utes such as coherency, autocorrelation, dip, azimuth, and
gradients were computed and attribute maps were issued
40 www.firstbreak.org © 2016 EAGE
Figure 16 (a) PP and PS stacks calibration at the well, (b) PP and PS stacks after event registration.
along the main horizons for structural interpretation. The
time structure maps extracted at the Top Ordovician from
the conventional P stack and from the PP stack show
similar structural features. However, when looking at the
two maps in more detail (Figure 14), we can see that
the PP stack, in spite of its slightly increased level of
noise, displays some structural events with an improved
lateral resolution and in some places additional subtle
lineaments.
Figure 15 also illustrates that the PP stack has improved
the power of detection of small faults and subtle lineaments
by comparing the two seismic datasets along a selected
cross-section.
An attempt was made to use P azimuth stacks to compute
the azimuthal anisotropy. Unfortunately the level of noise
was found to be too high to enable a reliable characterization
of potentially fractured zones.
PS Interpretation
One well with FMI data recorded in the Upper Ordovician
interval along with density, compressional and shear sonic
logs is available at the centre of the pilot survey area. At
Top Ordovician, the fractures interpreted from the FMI are
mainly induced fractures oriented NE-SW N40°-N50° and
some natural fractures (NNW-SSE N160°).
The seismic-to-well calibration of the PS stack was
performed together with event matching at the well loca-
tion due to the lack of S-wave check shot data (Hardage,
2011). However, the γ ratio profiles obtained from the
P- and S-sonic logs available in the TFT-West area were
used to finely calibrate the Hercynian Unconformity to Top
Ordovician interval. A constant value of 1.5 provided by the
well data was used in the Ordovician formation.
Owing to its easily spotted strong character on both
the PP and PS stacks, the Top Ordovician horizon was first
matched at the well location (Figure 16). After a residual
phase correction applied to the PS stack, the characteristic
waveform associated with the Hercynian Unconformity was
also used to verify the consistency of the PP and PS event
matching provided by the well γ ratio. After event matching
at the well, the PS stack cube was scaled to the PP-time scale and interpreted at this scale. Figure 17 Evidence of time delays between PS1 and PS2 components.
technical article first break volume 34, January 2016
By analyzing the PS seismic data after application of the
processing sequence described above, we observe the presence
of two anisotropic layers. The first one corresponds to the
interval between the Hercynian Unconformity and the Top
Frasnian with a maximum time delay of approximately 10 ms.
The second one relates to the Ordovician interval and displays
a maximum time delay of approximately 20-22 ms (Figure 17).
Frasnian Azimuthal anisotropy interpretation
The azimuthal anisotropy analysis performed within a com-
putation window of 200 ms centered on the horizon shows
orientations mainly in the N140°-N180° sector (NW-SE),
with an average time delay value of about 10 ms. Figure 18
shows the interpretation of this anisotropy which is per-
formed by superimposing the PS time delay map in colour
on the coherency map, in grey, extracted at the same level as
the PP stack.
The low time delay values recorded in the southern and
western parts of the pilot area suggest that, in the absence
of major faults, the anisotropy related only to the tectonic
stress is very small.
Conversely, the increase in time delay toward the fault
system located in the eastern part of the area and oriented
NE-SW is interpreted in terms of the presence of fractures;
the time delay is indicative of the fracture density that
noticeably varies throughout the study area. The average
anisotropy orientation is consistent with the general direc-
tion of open fractures in this region.
© 2016 EAGE www.firstbreak.org 41
technical article first break volume 34, January 2016
Figure 18 Interpretation of ani-
sotropy for Top Frasnian: (a)
Top Frasnian PP time structure
map (colour) with faults (red)
and lineaments(green) inter-
pretation superimposed with
coherency (grey), (b) PS time
delay map with PP faults (red)
and lineaments (green) inter-
pretation and (c) PS anisotropy
orientation map with PP faults.
Figure 19 Interpretation of
anisotropy for Top Ordovician:
(a) Top Ordovician PP data
t ime s t ruc t u re ma p (co l -
our) with faults (red) and
lineaments(green) interpreta-
tion superimposed with coher-
ency (grey), (b) PS time delay
map with PP faults (red) and
lineaments(green) interpreta-
tion and (c) PS anisotropy ori-
entation map.
The maximum time delay of about 20 ms observed at
the level of the major faults reveals the areas in which the
fracture density, probably correlated with the faulting, is
maximum. Fault relays also appear to be highly affected by
fractures. Nevertheless, some isolated spots with great time
delay values but not clearly related to faults could be inter-
preted as examples of fracture zones controlled by lithology.
Top Ordovician Azimuthal anisotropy study,
interpretation, and compensation
The radial and transverse component volumes obtained after
Frasnian anisotropy compensation were used as input to the
anisotropy analysis performed at the level of the Ordovician
reservoirs. The shear wave splitting anisotropy illustrated in
Figure 19 was used to characterize the fractures. The main
fracture orientations are found within the N100°-N160° sec-
tor consistent with the maximum horizontal stress while the
time delay reaches a maximum of 12 ms in the vicinity of a
complex fault system in the southern area. Some areas dis-
play a NE-SW orientation. As observed in the Frasnian, the
maximum fracture density is interpreted in the most faulted
area interpreted on the PP stack.
FMI shows that within Ordovician there are two zones
with the presence of open, induced, and cemented fractures:
42 www.firstbreak.org © 2016 EAGE
technical article first break volume 34, January 2016
(1) Top Ordovician with a low percentage of open fractures,
medium percentage of induced and cemented, and (2) from
2050 to 2070 m – 100 m below the top of the Ordovician
– where a large/high percentage of open fractures have been
observed. Based on this observation, it was decided to run
an anisotropy analysis test at 150 ms below Top Ordovician.
The main fracture orientation is clearly N160° (NNW-SSE)
measured from North and a 12 ms time delay on average.
Higher fracture density shown in high time delay values is
related to the fault system located at the South. At well loca-
tion, we observed a good tie between seismic and well data
in terms of open/cemented fracture orientation.
By correlating the fracture orientation and time delay
maps with FMI information recorded in the well, we observed
that at the well location the fractures detected by seismic
data are more related with induced fractures. However, at a
certain distance from the well location, open natural fractures
oriented N40°-50° (NE-SW) are present, indicating important
lateral variations in the fracture orientation and density. The
time delay map provides the same information with the low
density of open/cemented/induced fractures.
The PS anisotropy seems to be much less pronounced than
in the Devonian formations. At the Top Ordovician, most of
the pilot area displays weak time delays, indicative of a low
level of fracturing which is consistent with the small number
of fractures interpreted on the FMI data recorded in the well
located in the centre of the survey area. The only significant
anisotropy is found at the level of crossing faults in the
southern part of the area. As in the Frasnian, this increased
time delay is interpreted in terms of fracture density.
Conclusion
This first 3D-3C single sensor seismic pilot survey acquired
in Algeria proves to be a technology that provides PP-wave
seismic data that are, adequate for a detailed interpretation
of faults and fractures with an improved lateral resolution,
complemented by the shear wave splitting information from
PS-wave seismic data.
Good quali ty PP and PS datasets can be produced
by applying proper in-field acquisition QC as well as an
adequate processing sequence which includes an adapted
noise attenuation approach. Anisotropy parameters esti -
mated from PS data help to perform a fracture density and
orientation characterization of the overburden and reservoir.
Anomalies show an interesting relation with faults and
lineaments but the lateral extension of certain anomalies
suggests that fracture developments could also be controlled
by other parameters such as reservoir facies as suggested by
previous studies. 3D-3C seismic over the whole TFT West
area could provide a significant contribution to build a
complete fracture model which is currently not feasible with
well information alone.
Future work
Given the encouraging results of this first study, we recom-
mend further investigation of the reservoir characterization
aspects to:
n improve the S/N ratio of pre-stack PP seismic data and
perform an AVAZ (Amplitude versus Azimuth) analysis to
determine anisotropy parameters and compare them with
the results obtained using PS data,
n perform joint PP & PS elastic inversion and reservoir
facies characterization to better study the relationship
between reservoir quality, fracturing, and gas production.
Acknowledgements
The authors would like to thank CGG, GTFT, Sonatrach,
Repsol, and Total for their permission to publish this work.
They appreciate their support of the publication of this
3D-3C case history and their valuable input on the results.
The authors are grateful to S. Vanneste, J.M. Mougenot,
J. Cantillo and M. Girard from Total, and F.D. Martin from
Repsol for their support in the acquisition design, operations
coordination, acquisition monitoring, and data QC during
the acquisition. We would also like to acknowledge the
dedicated efforts of the CGG processing team.
And lastly, the authors would like to thank A. Müller and
E. Ramia from Total, C. Gordillo from GTFT, A. Bel-bachir
from Sonatrach for their support of this work.
The accomplishment of this project is proof of successful
cooperation.
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© 2016 EAGE www.firstbreak.org 43