3D-3C Multicomponent Seismic A successful fracture

$Page 1: 3D-3C Multicomponent Seismic A successful fracture$
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

© 2016 EAGE www.firstbreak.org 31

mailto:[email protected]

http://www.firstbreak.org/


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

32 www.firstbreak.org © 2016 EAGE


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.


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




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




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).




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°




A series of focusing analyses were produced for a set of constant effective gamma values ranging from 1.4 to 3.4, with




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.




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



Figure 11 Anisotropy results related to Top Ordovician.


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 interpretation study includes a PP pre-stack time-migrated stack,

referred to hereafter as the PP stack, a radial stack, referred



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



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.


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.




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:




(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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Received: 5 March 2015; Accepted 3 July 2015.

Doi: 10.3997/1365-2397.2016001



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