Orinoco Oil Belt Heavy Oil Interpretation

In preparation to use IHS Kingdom Software Jorge Acosta, PhD in Geophysics February 2011

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AbstractThis 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.

IntroductionThe 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,originandgeologyoftheOrinocoOilBelt(OOB)

• OrinocoOilBeltreservoirgeneralcharacteristics

• GeologicalandseismiccharacterofOOBproducingintervals

• Geophysicalcharacteristicsofheavyoilsandsands

• ExpectedperformanceofDirectHydrocarbonIndicators(DHI)fortheOrinocoOilBelt,withrecommendationsforseismicinterpretationusingIHSKingdomsoftware.

• FundamentalsonSpectralDecomposition(SD)

• Conclusions

Location, origin and geology of the Orinoco Oil Belt (OOB) 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 1: Venezuela Eastern Basin (red) and Orinoco Oil Belt (blue) (USGS, 2010)

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)

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.

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General features of Orinoco Oil Belt reservoirsFigure 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 permeability (1 to 17 D), medium temperature (100 to 135o F , or 40o C to 50o 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. Guzman-García, C.Huggins, L.Knauer, M.Minner, N.Kupsh, LuzM.Linares,

H.Rough, M.Waite, 2002/2003)

Geological and seismic character of producing intervalsSeismic 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)

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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:

• Somereflectionpolaritiesaredubious,likeforSANDI

• Ligniteshaveastrongandoftenextendedseismicresponse

• Predominantfrequencyislowandscarcelyresolvesthickerstrata,asinSANDIIandIII

Geophysical characteristics of heavy oils and sandsConcepts 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).

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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 (c) show that oil studied samples have MP values between 50o C and 70o C.

Above that, P-wave velocity increases linearly with decreasing temperature (Figures 7(a) and (c)), while the S wave is practically nonexistent for temperatures above 50 to 70o 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.

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At temperatures below the melting point (MP = 60o C to 80o 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 temperature at a rate of about 10(m / s) / oC. At temperatures greater than MP, velocity decreases almost linearly (3 to 4 (m / s) / oC) 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.

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 Indica-tors for the Orinoco Oil Belt, and recommendations for seismic interpretationThe 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.

DHI Observations about DHI in OOB Recommendations

1 Amplitude above background (Bright Spot)

Lignites are common and they have a strong and extended response

• 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

2 Seismic data versus expected response

• Often, but mainly at shallow depths:o Reflection polarity is dubiouso Velocity changes depend little on lithologyo 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

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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.

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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.

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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 net-pay 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 development of this work was based on theoretical evidence for reduced seismic velocities in the presence of high (GOR) gas content in heavy oils (De-hua Han, Jiajin Liu,, Micheal Batzle,, 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 free gas over the oil. Results are shown in Figure 11 (left).

The workflow was as follows:

1.Spectraldecompositionofseismichorizonsrelatedtodepositsoftheupperpartofthereservoir.Gasdetectioninthesectionof50Hzspectraldecomposition,whichmarkedthelateralextensionofgasandGOC,afterdepthcalibrationofgascontentinboreholelogsandoilproductionfromwellsinsidethisarea.

2.Todifferentiatebetweenamplitudeswithandwithoutgas,adetuningcurvewasconstructedbyremovinghighamplitudes,associatedtogas,fromtheTimevs.Amplitudecurve.

3.APseudo-impedanceinvertedcubewasobtainedfromdetuneddataandinterpreted.

4.Lowimpedancesprovedtocorrespondtosanddistribution.CombinationofdetunedamplitudesandmapobtainedimpedanceallowedthedetectionofnetsandbelowtheGOCandabovetheOWC.Thosesandswereinterpretedtobeoilbearing.

SMT - Intuitive. Integrated. Interpretation. Page 11

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 net-pay 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 development of this work was based on theoretical evidence for reduced seismic velocities in the presence of high (GOR) gas content in heavy oils (De-hua Han, Jiajin Liu,, Micheal Batzle,, 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 free gas over the oil. Results are shown in Figure 11 (left).

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.

Interpret horizons

Select interval

Compute spectra (TracePAK)

Normalize amplitude in foil

x y T

x

x

T y

x

freq

y

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Figure 11: Uracoa Field net pay targets selected for two new drilling locations by Spectral Decomposition and Pseudo-Impedance 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)

Another procedure to use Spectral Decomposition for interpretation, probably more robust, is outlined by (Roden, 2008) and employed in IHS Kingdom software:

1. Selecttheareaofinterestontheamplitudesinterpretationsection.

2. CalculatethefrequencyspectrumintheareaofinterestwithTracePAK.

3. SelecttheattributeinRSASpectralDecomposition(DecompSpectralAttributes),asthepicturewindowinFigure12shows.

4. Initiallydetermineareasonablenumberofsub-bands(e.g.5to10).

5. Selectthestartandendfrequenciesofeachsub-band,accordingtothespectrumofdata,ensuringthatsub-bandsextremesaremostlyincludedinthebandwidthofdata.Itisachievedbychoosingtypeandnumberofsub-bands.

6. Locatethesub-bandwheretheseismiceventofinteresthasthehighestamplitudeand,foritsCentralFrequency(FC),calculatethetuningthickness(TT=wavelength/4)fortheseismicvelocity(V)intheevent.Since:

Figure 12: IHS Kingdom RSA Spectral Decomposition Analysis screens with 10 sub-bands linearly chosen between 5Hz and 90Hz, taken from (Roden, 2008)

7. Theselectionoflineardivisionofthebandwidthofthedataproducesthespecifiednumberofbandsofequalwidth.Thewidthdependsonthenumberchosen.AnexampleisshowninFigures12and13.

8. Thechoiceofsplittinginoctavesthedatabandwidth,showninFigure13atthetop,producesthespecifiednumberofbands,withincreasingwidth.Thetotalwidthofeachbandistheoctaveofthecenterfrequency(FC).Therefore,ifFC=90Hz,thewindowofthisbandextendsfrom45Hzto135Hz.ButifFC=13Hz,thewindowrangesfrom6.5Hzto19.5Hz.

9. However,inbothtypesofscales,significantlimitsforeachsub-bandarethefrequenciesatwhichtheamplitudeofthewindowreacheshalfthemaximum.

10.Alwayscalipercalculatedthicknesseswithavailablewellinformation.

11.Ifnecessary,redefinethenumberofsub-bandsandtheirwidthstobetterdefinethethicknessoftheobjectivestointerpret.

12.Theoutputofsub-bandsbyfrequencybandwrapsisagoodmethodtoidentifythehighamplitudesthatmayindicatethespecificfrequenciesofintonation.

13.Thesub-frequencybandsthatarenotfullyincludedwithinthefrequencybandwidthofseismicdataproducewrongfrequencytuning,especiallyforlowfrequencies.14.Thelineardecompositionintonarrowsub-bands(e.g.<4or5Hz)cancauseringing,thatisbrokenoroscillatingtuning,especiallyforhighfrequencies.

15.Thelineardecompositionintosub-octavebandstendstoproducegoodresultsforlowandmediumfrequencies,butitswidthathighfrequenciescanleadtosomethicknesslackofdefinition.

V = Frequency * Wavelength g TT = (V / FC) / 4

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Figure 13: Banding by octaves (top) and linear banding (bottom) with seismic spectrum overlaid, from (Roden, 2008)

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).

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 funnel-mouth 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.

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. Aseismicdataintervalbetween1and2secondswasinterpretedusingsoftwareSpectralDecomposition(SD)andRockSolidAttributes(RSA)ofSeismicMicro-TechnologyInc.IHSKingdomSoftware.

2. DecompositionattributeswereselectedfromSDEnvelopeAttributesSub-bandwithautomaticsettingstocreatetensub-bandsonalinearscale.Afterexaminingthevarioussub-bands,itwasnotedthattheseismicvolumeoftheSDEnvelopeSub-bandcenteredon32Hz(tuningof100feetthick),inFigure11(Right),showsDSandsgrossdepositionalnature,apparentlyimagingthebroaderandthickeroverallvalleyfilldeposits,representedbytheyellowandorangeshownintherightsideofFigure11,butthefluvialvalleyfillcomplexisstillnotclearlydefined.

3. Todefinewhatisthoughttobethevalleyfillarchitecture,KingdomRSAGeometricAttributeswererunovertheSDEnvelopeSub-band_32volumebetween1.4and1.6seconds.Geometricattributesrespondtochangesinreservoirstructureandstratigraphy.

4. InstantaneousDipoftheSDEnvelopeSub-band_32volumegavethebestpresentationofwhatisbelievedtobethevalleyfillarchitecture.ResultsaredisplayedinFigure14(Left),withclippedopacitysothattheassumedvalleyfillcomplexremainsopaqueandtherestofthevolumebecomestransparent.

5. Additionally,themixingfunctionor“coblending”ofattributesSDInstantaneousDipandSDShaleIndicatorwasusedtoenhancethepositionofthesandyareas,asshowninFigure14(Right),wherebrightcolorsarebelievedtobeassociatedwiththevalleyfillwhilethedarkercolorsareindicativeofshales.

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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:Weobservedthemovementofhighamplitudesfromverythicktothinnerpartsasfrequencyincreases.AmplituderesponsesandSDresultsdependonthetypeofsedimentation.Adequateknowledgeofthegeologyoftheareaiscriticaltounderstandingeneralwheretherecouldbehydrocarbons.

• Direct hydrocarbon indicator:modelingoftheseismicresponseandSDtechniquemightbeusedtodeterminetheeffectofoilonthefrequencyspectrumoftheformationsofinterest.Butthespecificfrequenciesforsaturatedhydrocarbonlayersmaydependinpartonthecharacteristicsofhydrocarbons.Detailedanalysisofwelllogsandcontemporaryseismicsurveywouldreflectreservoirconditions.Productioninformationisimportanttoverifythatamplitudeanomaliescorrespondtooilandnottootherfactors.

• Thin gas reservoir indicator: byanalysisofattenuationandlowfrequencyshadowsbeneathathinlayer,thepossiblepresenceofhydrocarbonsisindicated.

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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• SeismicinterpretationoftheOrinocoOilBeltrequires

specialcareduetoitselusivesandbodies,theirdubiousimpedancecontrast,andbecausetheirseismiccharacterisstronglydependentoncharacteristicsoftheheavyoilthatsaturatesthem.

• ItisexpectedthattheresourcesofSpectralDecompositionandotherattributes,suchasAcousticandElasticImpedanceinvertedtracesorvolumes,wouldallowdiscriminationofreservoirsandscharacteristicsandtheirstructures.

• Specialcaremustbeplacedoncorrelationandcalibrationwithwellinformationofallkinds,fromgeological,petrophysicalandgeophysicaluptoproductiondata.

• Theseismicdatashouldbeofthehighestqualityintermsofminimizingnoiseandmaximizingthefrequencyband.

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