Orinoco Oil Belt Heavy Oil Interpretation

Orinoco Oil Belt Heavy Oil Interpretation
In preparation to use IHS Kingdom Software
Jorge Acosta, PhD in Geophysics February 2011
Abstract
This work reviews some geological and geophysical
characteristics of the crude oil and the reservoir rocks in the
Orinoco Oil Belt. Expected seismic responses are also analyzed,
as well as seismic data. Usual conditions and recommendations
are given about the use of Direct Hydrocarbon Indicators
(DHI) and the use of IHS Kingdom attributes and Spectral
Decomposition to facilitate the interpretation.
Introduction
The singular characteristics of Orinoco Oil Belt heavy oil reservoirs
and their strategic importance invite a study and preparation of
the resources available for the interpretation of the seismic data.
The goal is to improve the performance of future interpretation
work using IHS Kingdom software.
Topics index
• Location, origin and geology of the Orinoco Oil Belt (OOB)
• Orinoco Oil Belt reservoir general characteristics
• Geological and seismic character of OOB producing intervals
• Geophysical characteristics of heavy oils and sands
• Expected performance of Direct Hydrocarbon Indicators
(DHI) for the Orinoco Oil Belt, with recommendations for
seismic interpretation using IHS Kingdom software.
• Fundamentals on Spectral Decomposition (SD)
• Conclusions
Location, origin and geology of the Orinoco Oil
Belt (OOB)
Figure 2: Schematic cross section of the Venezuela Eastern
Basin (USGS, 2010)
The thrust over the platform was episodic and slow. Therefore,
download river systems from the Guiana Shield had enough time
to maintain their general course, flowing from south to north,
across the slowly increasing peripheral thickening of the thrust,
forming a rather flat topography of fluvial, coastal, and tidal origin
(Bejarano, 2006), (Justiniano, 2007) (Bartok, 2003) that later
became Miocene sandstones. They were saturated by the oil
coming from the depths of the northern basin, and the oil lost
the lighter fractions to form heavy oil reservoirs in the Orinoco
Oil Belt, characteristic of the Oficina Formation. Those reservoir
sandstones, although porous and permeable, are formed by a
variable number of depositional sequences with considerable
internal flow heterogeneity caused by different facies and shale
barrier juxtapositions that reduce recovery efficiency, as is
illustrated in Figure 3 (Srinivasan). (Maria I. Jácome, Nick Kuszni,
Felipe Audemard y Steve Flint, 2003)
The Orinoco Oil Belt (see Figure 1, in blue) is a large oil field in
the Eastern Basin of Venezuela (in red) located on the left bank of
the Orinoco River, Venezuela. As Figure 1 shows, the Orinoco Oil
Belt extends east-west for approximately 650 km and some 70
km north to south, for a total area of 55.314 square kilometers.
(USGS, 2010)
The Eastern Basin is a foreland basin (Figure 2), created by the
diagonal collision and thrusting of the Caribbean Plate over the
South American Plate, at the end of the Pliocene and during the
Miocene. (F.Yoris y M. Ostos, 1997)
Figure 3: Schematic view of channels juxtaposition (Srinivasan)
In (L.E.V., 2007) the Oficina Formation is described as an
alternation of gray shale, intercalated and inter-bedded with
sandstones and light-colored, fine to coarse grained siltstones.
Minor components, but important units, are thin layers of lignite,
carbonaceous shale, and thin limestones. Carbonaceous
materials are common. In general, sandstones become more
abundant, thicker and coarser-grained towards the base of
the formation.
Figure 1: Venezuela Eastern Basin (red) and Orinoco Oil Belt
(blue) (USGS, 2010)
2
General features of Orinoco Oil Belt reservoirs
Figure 4 illustrates the general properties of the reservoirs. It
shows a table of typical reservoir values, and a representative
set of borehole logs built for the central part of the Orinoco Belt.
(C.Curtis, R.Kopper, E. Decoster, A. Guzman-García, C.Huggins,
L.Knauer, M.Minner, N.Kupsh, LuzM.Linares, H.Rough, M.Waite,
2002/2003)
The reservoirs tend to be sands, relatively shallow (about
2000 feet), unconsolidated, with high porosity (30 to 35%) and
o
permeability (1 to 17 D), medium temperature (100 to 135 F , or
o
o
40 C to 50 C), saturated with heavy oil (often of higher density
than water), low gas content (60 to 70 pc / bbl), relatively low
dynamic viscosity (1200 to 2000 cp), low compressibility (80 to 90
* 10-6 psi-1) and low initial pressure (600 to 900 psi).
The set of gamma ray and resistivity typical logs of the Orinoco
Belt was built by gathering pieces to illustrate the signature of the
sands and their associated characters. Thick sandstones of high
resistivity, like those marked A and B in the figure below, most
likely originated from a river environment depositing well sorted
sands relatively clean of clay. However, sand packages that show
resistivity and gamma ray irregular profiles, as in C, correspond
to changing thinner sands with bands of shale, characteristics of
a greater marine influence, as sediments deposited in deltaic and
tidal flats environments.
Figure 4: Typical reservoir values, and a representative set of
borehole logs (C.Curtis, R.Kopper, E. Decoster, A. GuzmanGarcía, C.Huggins, L.Knauer, M.Minner, N.Kupsh, LuzM.Linares,
Geological and seismic character of
producing intervals
Seismic response of the rock depends on the impedance change,
that is, on velocity and density changes.
The left image in Figure 5, modified from one in (Balaguera, 2010),
shows some profiles measured in a borehole. They are, from left
to right: depths (MD), gamma ray (GR), density (ZDEN), sonic
(DT), acoustic impedance and reflectivity. In clean sands density
values are near 1.9 g/cm3, while density for shales can reach
values of 2.3 g/cm3, and lignites about 1.3 g/cm3, making lignites
very strong reflectors, as the reflectivity column indicates. Most
high reflectivity values correspond to possible lignites (low values
of density and GR), while most sands show low reflectivity.
Typical logs and properties of reservoirs in the Orinoco Belt
(Zuata).The sand stones of good thickness and high resistivity
(A and B) most likely come from fluvial environments,while more
irregular and thinner sands (C) had stronger marine influence.
Figure 5: Example of Velocity, Density and Reflectivity values
(Balaguera, 2010)
H.Rough, M.Waite, 2002/2003)
3
Figure 6: Example of seismic response of lignites and sands
(Delineado de canales con VSP, 2002)
Figure 5 modified from one in (Balaguera, 2010), presents an
example of typical seismic velocity values in rocks of the Orinoco
Belt. Lack of compaction causes low velocity values of about
2100 m/s (7000 f/s), that increase with depth at an approximate
rate of a thousand feet per second for each 400 feet.
Figure 6 compares borehole logs and seismic response of a VSP
Zero Offset. We observe:
• Some reflection polarities are dubious, like for SAND I
Geophysical characteristics of heavy oils and sands
Concepts and graphics presented in this section were taken from
(M. Batzle, R. Hofmann, D-h. Han, 2006), (De-hua Han, Jiajin Liu,,
Micheal Batzle,, 2007), (De-hua Han, Hui-zhu Zhao, Qiuliang Yao,
Michael Batzle, 2007) and (De-hua Han, Jiajin Liu, Michael
Batzle, 2006)
Heavy oil presents in three phases: solid (crystal), semi liquid
and liquid. The properties of each phase depend strongly on oil
composition and temperature (Figure 7 (a), (b) and (c), and to a
lesser extent, on pressure (Figure 7 (c).
• Lignites have a strong and often extended seismic response
• Predominant frequency is low and scarcely resolves thicker
strata, as in SAND II and III
4
Figure 7: (a) and (c): P wave velocity of heavy oil, (b) S wave
velocity of heavy oil, (d) P wave velocity of heavy oil sand samples,
all taken from references cited before
Heavy oil properties are similar to the properties of light oil for
temperatures above the melting point (MP). Figures 7(a), (b) and
o
(c) show that oil studied samples have MP values between 50 C
o
and 70 C.
Above that, P-wave velocity increases linearly with decreasing
temperature (Figures 7(a) and (c)), while the S wave is practically
o
nonexistent for temperatures above 50 to 70 C (Figure 7(b)).
By decreasing the temperature below MP, heavy oil changes its
phase from low viscosity liquid to quasi-solid phase, drastically
increasing its viscosity. Then, S-wave velocity becomes
measurable and P-wave velocity deviates increasingly from light
oil trend, with higher gradient value.
Even cooler, it goes into the glass phase, when the viscosity
increases beyond the point of crystallization, defined by the
value of 1013 Poise. At low temperature, heavy oil has its highest
viscosity and acts physically as a solid, with corresponding
effective shear modulus and S wave propagation.
Vp and Vs values in Figure 7(a) and (b) respectively, reveal that
heavy oils with lower API present higher MP temperature, higher
values of Vp and Vs, and higher P wave velocity deviation from
the liquid phase trend.
Heavy oil elastic modules and seismic velocities are strongly
dependent on temperature and frequency. Therefore, heavy oil
properties measured in the ultrasonic range (105 to 106 Hz), sonic
range (104 Hz), and seismic range (10 to 100Hz), could give very
different values.
Heavy oil velocities for P and S waves (Vp and Vs) show a
systematic relationship with API gravity, temperature, pressure,
GOR, and are dispersive when heavy oil is in its quasi-solid state.
Seismic velocities in heavy oil saturated sands are mainly
controlled by oil properties.
Figure 7(d) presents a collection of acoustic velocity
measurements on samples from Alberta (Measured Velocity
Data on Heavy Oil Sands, 2008); it shows clearly that heavy oil
sand seismic velocity is mainly controlled by heavy oil properties.
P and S wave velocities in heavy oil decrease with increasing
temperature, and also do it in sand saturated with it.
5
o
o
At temperatures below the melting point (MP = 60 C to 80 C)
heavy oil is solid or near-solid. It is seismically dispersive and
highly attenuating. Likewise, the P wave velocity in sandstone
samples saturated with this heavy oil decrease with increasing
o
temperature at a rate of about 10(m / s) / C. At temperatures
greater than MP, velocity decreases almost linearly (3 to 4 (m / s) /
o
C) and its dispersion is negligible.
(De-hua Han, Hui-zhu Zhao, Qiuliang Yao, Michael Batzle, 2007)
Analyzed effective rock shear module increase with heavy oil
saturation, and proposed that the main factors affecting velocity
are related to changes in oil viscosity and shear modulus, and
heavy oil interaction with rock grains. Velocity reduction in the
solid and near-solid phases corresponds to viscosity and shear
module’s gradual decrease with increasing temperature. The
abrupt transition of these properties at the MP explains the
changing interaction between heavy oil and sand grains. It goes
from being part of the rock matrix, at MP oil becomes a pore fluid.
For temperatures beyond MP oil has very little resistance to shear.
DHI
Observations about DHI in OOB
There is also a frequency dependence of the properties: the
higher the frequency, the greater the rigidity.
Furthermore, Gassmann equations describe well the velocity
of the sand for temperatures corresponding to heavy oil liquid
phase, but produce smaller than measured velocity values for the
solid and quasi-solid oil phase intervals.
Expected performance of Direct Hydrocarbon Indicators for the Orinoco Oil Belt,
and recommendations for seismic interpretation
The first column of the following table presents eight general
Direct Hydrocarbon Indicators (DHI) suggested by (Regueiro,
2010). Some comments about their application for Orinoco Oil
Belt case are in the second column, and in the third column there
is a list of some suggestions to optimize DHI use.
Recommendations
1
Amplitude above
background (Bright Spot)
Lignites are common and they have a strong
and extended response
2
Seismic data versus
expected response
• Often, but mainly at shallow depths:
o Reflection polarity is dubious
o Velocity changes depend little on lithology
o Impedance changes depend more on
density variations
• Sand/Shale Impedance contrasts are
usually small
• Low dominant frequency that often only
resolves gross sand intervals
• P and S velocities in oil sands are very
sensitive to temperature
3
Frequency / Character
change at fluids contacts
• Fluid Impedance contrast could be very little
for temperatures near and over MP,
but could be strong for temperatures
lower than MP
• Near MP, seismic frequency response
could be very different from acoustics
or ultra-acoustics
• Increase seismic data amplitudes and frequency
band through Inversion and AVO if necessary
• Lithology and saturation estimation from inverted
seismic data
4
Amplitude and/or
Frequency shadows
Mainly for middle to high seismic frequencies
(e.g. 50Hz)
• Structure and strata definition using Spectral
Decomposition and other attributes
• Attribute analysis according to frequency
and temperature expected seismic response
5
Conformation to structure
• Small dips and liquid oil small contrast
hide unconformities.
• Water could be over oil
6
Flat Spots
Thin sands, small dips and low contrast could
hide flat spots
7
Velocity Push Down of
time image
Difficult because most sands are thin and
velocities generally low
8
Gas Chimney
No example known in OOB
• Careful editing and calibration of borehole logs
• Identify and characterize every seismic response
at borehole location
• Identify important horizons and responses
• Be aware of sands temperature in wells
• Elastic variables definition for lithology and
saturation estimation
• AVO response type definition for oil sands
and important reflectors
6
The first four recommendations in the table left (log edition
and calibration, identification and characterization of reflectors,
and temperature ranges) are convenient procedures for any
interpretation, but they have special importance in the OOB
reservoirs because the sedimentary sequence of producing
intervals is normally in a window characterized by rapid increase
of compaction with abundant presence of hydrocarbons and
lignites, that hinder the usual application of seismic clues about
polarity and strength of impedance contrasts.
The general use of diverse attributes is a must because frequency
ranges common in seismic data are rather low to separate the
top and base of most sands. Often only gross sands appear well
resolved, but they tend to be water sands because of their high
porosity and permeability, low dip and lack of shale barriers.
Figure 8: attributes to enhance the effect of fluid presence.
(SMT, 2010)
Several attributes may be employed (Hart @SMT), (Roden, 2008).
For example, SMT’s Kingdom TracePAK can generate Running
Sum to enhance changes in horizontal pattern, since this attribute
is the addition or integration of the amplitude values with depth
(Figure 8).
Functions in Kingdom’s Rock Solid Attributes (RSA) could also be
used to generate attributes that help to see a change in frequency
and/or character. As shown in Figure 8, the instantaneous
attribute Weighted Envelope Frequency and the normalized
amplitude of cosine of instantaneous phase (Normalized
Amplitude (Cosine of Instantaneous Phase)) help to identify a
hydrocarbon indication feature.
Spectral Decomposition (SD) and Instantaneous Spectral
Analysis (in Kingdom RSA) are particularly useful for stratigraphic
interpretation of random deposits, as the abandoned distribution
channels of rivers and deltas.
7
Fundamentals on Spectral Decomposition
Spectral Decomposition is based on the “Tuning” of signal
frequency and sand thickness. Its physical basis is the
interference of wave patterns generated by the top and bottom
of the sand. For a certain wavelength, the interference between
reflections of opposite sign sum up, producing a bright spot
in terms of thickness. In other words, the frequency content
modulates the amplitude of the seismic data, and highlights
the image of the stratigraphic thickness related to data
dominant frequencies.
Figures 9 - I, II and III present an explanatory sketch taken from
(Spectral Decomposition for Seismic Stratigraphic Patterns,
2003). Figure 9-I presents a stratigraphic feature that varies in
thickness. If the frequency content is high (eg 30Hz), thicknesses
“tuned” and highlighted with the highest amplitude are thin (in
turquoise), as in Figure (II). If bandwidth is dominated by low
frequencies (eg 15Hz) thicker stratigraphic aspects are featured
(in red), as in Figure (III).
Figure 9: Spectral Decomposition explanatory sketch taken from
(Spectral Decomposition for Seismic Stratigraphic Patterns, 2003)
The procedure to obtain Spectral Decomposition for interpretation
is outlined in Figure 10, modified from (Partyka, G.A., Gridley,
J.M., and Lopez, J., 1999). The two-way-time Sub-volume of
interest (x,y,T), limited by selected horizons in the Interpreted
3D seismic volume, is Fourier transformed to produce an
instantaneous spectral volume (x,y,f) where the tuning to
dominant frequency should be evident. That Tuning Cube
consists of three components: thin bed interference, multiplied by
wavelet overprint, plus noise. By balancing the wavelet amplitude
without degrading the geological information, the tuning cube
is reduced to thin bed interference and noise. Amplitude is
normalized in each (x,y,f) foil or frequency slice, to minimize
the amplitude modulation due to the wavelet effect, and they
are passed, observing one after another, as a movie, to detect
amplitude tunings.
8
Figure 10: Spectral Decomposition procedure, modified from
(Partyka, G.A., Gridley, J.M., and Lopez, J., 1999).
An example of the application of this procedure to the
determination of gas-oil (GOC) and oil-water (OWC) contacts
in a field near the OOB can be found in (Fluid contacts and netpay identification in three phase reservoirs, 2009). This report
describes a comprehensive interpretation of two 3D seismic
attributes: the spectral decomposition and pseudo-impedances
inversion of 165 km2 reprocessed 3D seismic data, to identify
fluid contacts (GOC and OWC) of heavy oil reservoirs in the Field
Uracoa near OOB, south of Monagas in eastern Venezuela. See
Figure 19 (Fig. 5 in mentioned reference).
The workflow was as follows:
1. Spectral decomposition of seismic horizons related to
deposits of the upper part of the reservoir. Gas detection in
the section of 50Hz spectral decomposition, which marked
the lateral extension of gas and GOC, after depth calibration
of gas content in borehole logs and oil production from wells
inside this area.
2. To differentiate between amplitudes with and without gas, a detuning curve was constructed by removing high amplitudes, associated to gas, from the Time vs.
Amplitude curve.
3. A Pseudo-impedance inverted cube was obtained from
detuned data and interpreted.
4. Low impedances proved to correspond to sand distribution.
Combination of detuned amplitudes and map obtained
The development of this work was based on theoretical evidence
impedance allowed the detection of net sand below the for reduced seismic velocities in the presence of high (GOR) gas
GOC and above the OWC. Those sands were interpreted content in heavy oils (De-hua Han, Jiajin Liu,, Micheal Batzle,,
to be oil bearing.
2007), and hope that gas accumulations were detectable, since
their seismic contrast could be observed in borehole logs of
several wells: two wells where high GOR (> 24000 scf / barrel)
was produced, and some others
that- presented
gas over Interpretation.
the
SMT
Intuitive. free
Integrated.
Page 11
oil. Results are shown in Figure 11 (left).
Interpret horizons
T
y
x
xT
fre
q
yx
Select interval
Compute spectra
(TracePAK)
Normalize amplitude in foil
Modified from: Partyka, G.A., Gridley, J.M., and Lopez, J. 1999,
Interpretational Applications of Spectral Decomposition in Reservoir
Characterization, The Leading Edge, vol. 18, No. 3, pg 353-360.
omposition procedure, modified from (Partyka, G.A., Gridley, J.M., and Lopez, J., 1999).
e application of this procedure to the determination of gas-oil (GOC) and
contacts in a field near the OOB can be found in (Fluid contacts and netin three phase reservoirs, 2009). This report describes a comprehensive
two 3D seismic attributes: the spectral decomposition and pseudoion of 165 km2 reprocessed 3D seismic data, to identify 9fluid contacts
of heavy oil reservoirs in the Field Uracoa near OOB, south of Monagas in
V = Frequency * Wavelength g
Figure 11: Uracoa Field net pay targets selected for two new
drilling locations by Spectral Decomposition and PseudoImpedance Interpretation, taken from (Fluid contacts and
net-pay identification in three phase reservoirs, 2009).
Right: Sooner “D” Sand gross depositional nature shown
by SD Envelope Sub-band of 32 Hz at 1.456 seconds,
taken from (Hart @SMT)
TT = (V / FC) / 4
Figure 12: IHS Kingdom RSA Spectral Decomposition Analysis
screens with 10 sub-bands linearly chosen between 5Hz and
90Hz, taken from (Roden, 2008)
7. T
he selection of linear division of the bandwidth of the data
produces the specified number of bands of equal width.
The width depends on the number chosen. An example is
shown in Figures 12 and 13.
8. T
he choice of splitting in octaves the data bandwidth,
shown in Figure 13 at the top, produces the specified
number of bands, with increasing width. The total width
of each band is the octave of the center frequency (FC).
Therefore, if FC = 90 Hz, the window of this band extends
from 45Hz to 135Hz. But if FC = 13Hz, the window ranges
from 6.5Hz to 19.5Hz.
Another procedure to use Spectral Decomposition for
interpretation, probably more robust, is outlined by
(Roden, 2008) and employed in IHS Kingdom software:
1. S
elect the area of interest on the amplitudes interpretation section.
2. C
alculate the frequency spectrum in the area of interest with TracePAK.
9. H
owever, in both types of scales, significant limits for each
sub-band are the frequencies at which the amplitude of the
window reaches half the maximum.
3. S
elect the attribute in RSA Spectral Decomposition
(Decomp Spectral Attributes), as the picture window
inFigure 12 shows.
10.Always caliper calculated thicknesses with available well information.
4. Initially determine a reasonable number of sub-bands (e.g. 5 to 10).
11.If necessary, redefine the number of sub-bands and their
widths to better define the thickness of the objectives to interpret.
5. S
elect the start and end frequencies of each sub-band,
according to the spectrum of data, ensuring that sub-bands
extremes are mostly included in the bandwidth of data. It is achieved by choosing type and number of sub-bands.
12.The output of sub-bands by frequency band wraps is a
good method to identify the high amplitudes that may
indicate the specific frequencies of intonation.
6. L
ocate the sub-band where the seismic event of interest
has the highest amplitude and, for its Central Frequency
(FC), calculate the tuning thickness (TT = wavelength / 4) for the seismic velocity (V) in the event. Since:
13.The sub-frequency bands that are not fully included within
the frequency bandwidth of seismic data produce wrong
frequency tuning, especially for low frequencies. 14. The
linear decomposition into narrow sub-bands (e.g. <4 or 5
Hz) can cause ringing, that is broken or oscillating tuning,
especially for high frequencies.
15.The linear decomposition into sub-octave bands tends to
produce good results for low and medium frequencies, but
its width at high frequencies can lead to some thickness
lack of definition.
10
Figure 13: Banding by octaves (top) and linear banding (bottom)
with seismic spectrum overlaid, from (Roden, 2008)
3. T
o define what is thought to be the valley fill architecture,
Kingdom RSA Geometric Attributes were run over the
SD Envelope Sub-band_32 volume between 1.4 and 1.6
seconds. Geometric attributes respond to changes in
reservoir structure and stratigraphy.
If seismic data frequency band is shorter than required to show up
sediment structure, and spectral decomposition “per se”: is not
able to discriminate the target structures, then additional attributes
can be combined with spectral decomposition, as is explained in
the following example taken from (Hart @SMT).
4. Instantaneous Dip of the SD Envelope Sub-band_32 volume
gave the best presentation of what is believed to be the
valley fill architecture. Results are displayed in Figure 14
(Left), with clipped opacity so that the assumed valley
fill complex remains opaque and the rest of the volume
becomes transparent.
The interpretation in (Hart @SMT) was performed as part of an
advanced secondary recovery project for Sooner “D” Sand Unit,
a Cretaceous sand stratigraphic trap located in Weld County,
northeast Colorado USA. The deposit is considered as a funnelmouth estuary and valley fill, consisting of a series of vertically
stacked channels, most of them 30 feet thick or less each, with an
individual maximum thickness of 70 feet. The D Sand is located
seismically at approximately 1.45 sec. two-way time.
5. A
dditionally, the mixing function or “coblending” of
attributes SD Instantaneous Dip and SD Shale Indicator was
used to enhance the position of the sandy areas, as shown
in Figure 14 (Right), where bright colors are believed to be
associated with the valley fill while the darker colors are
indicative of shales.
Spectral analysis showed that the interval around the Sand D, has
a dominant frequency close to 55Hz, so tuning thickness is 61
feet ((13,500 ft / sec / 55Hz) / 4) and most of the thicknesses are
below this. Nevertheless, the interpretation was attempted in the
following way:
1. A
seismic data interval between 1 and 2 seconds was
interpreted using software Spectral Decomposition (SD) and
Rock Solid Attributes (RSA) of Seismic Micro-Technology
Inc. IHS Kingdom Software.
2. D
ecomposition attributes were selected from SD Envelope
Attributes Sub-band with automatic settings to create ten
sub-bands on a linear scale. After examining the various
sub-bands, it was noted that the seismic volume of the
SD Envelope Sub-band centered on 32Hz (tuning of 100
feet thick), in Figure 11 (Right), shows D Sands gross
depositional nature, apparently imaging the broader and
thicker overall valley fill deposits, represented by the yellow
and orange shown in the right side of Figure 11, but the
fluvial valley fill complex is still not clearly defined.
11
Figure 14: Instantaneous Dip of the SD Envelope Sub-band_32
volume gave the best presentation of what was believed to be
the valley fill architecture. Right: “coblending” of attributes SD
Instantaneous Dip and SD Shale Indicator, where bright colors are
believed to be associated with the valley fill while the darker colors
are indicative of shales. (Hart @SMT)
Recommendations about Spectral Decomposition
Spectral Decomposition (SD) and Instantaneous Spectral
Analysis (SRA) can be used as:
• Thin sands detector: We observed the movement of high
amplitudes from very thick to thinner parts as frequency
increases. Amplitude responses and SD results depend
on the type of sedimentation. Adequate knowledge of the
geology of the area is critical to understand in general where
there could be hydrocarbons.
• Direct hydrocarbon indicator: modeling of the seismic
response and SD technique might be used to determine
the effect of oil on the frequency spectrum of the
formations of interest. But the specific frequencies for
saturated hydrocarbon layers may depend in part on the
characteristics of hydrocarbons. Detailed analysis of well
logs and contemporary seismic survey would reflect reservoir
conditions. Production information is important to verify
that amplitude anomalies correspond to oil and not to other
factors.
• Thin gas reservoir indicator: by analysis of attenuation and
low frequency shadows beneath a thin layer, the possible
presence of hydrocarbons is indicated.
12
In order to obtain more precise and punctual results it is
essential to know in detail the process of acquiring seismic data,
the processing parameters and, in general, to have as much
processed seismic data as possible.
Conclusions
• Seismic interpretation of the Orinoco Oil Belt requires special care due to its elusive sand bodies, their dubious
impedance contrast, and because their seismic character is
strongly dependent on characteristics of the heavy oil that
saturates them.
• It is expected that the resources of Spectral Decomposition and other attributes, such as Acoustic and Elastic Impedance inverted traces or volumes, would
allow discrimination of reservoir sands characteristics and
their structures.
• Special care must be placed on correlation and calibration
with well information of all kinds, from geological,
petrophysical and geophysical up to production data.
• The seismic data should be of the highest quality in terms of minimizing noise and maximizing the frequency band.
13
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