quantitative assestment of the seismic net-pay method.pdf
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Quantitative assessment of the seismic net-pay method: A case study
Ramses G. Meza1, Juan M. Florez1, Stanislav Kuzmin1, and John P. Castagna2
Abstract
We applied the seismic net-pay (SNP) method to an oil discovery and predicted thicknesses consistent withthe actual thicknesses at the wellbore locations. This was accomplished by applying the method in a self-cal-ibrating mode that did not require the direct use of well information. For net-pay estimation under a self-cal-ibration scenario, the SNP method thickness estimates proved to be more accurate (mean absolute predictionerror at well validation locations under 3.0� 1.5 m) than estimates from a reflectivity-based detuning method(4.0� 2.7 m) or multiple linear regression (5.9� 5.8 m). Statistical F -tests indicated that the correspondencesof the predicted thickness estimates with actual net-pay values for the SNP and reflectivity methods (F approx-imately 5.5–6 for both) were statistically significant, whereas the multiple regression results did not prove to bestatistically significant.
IntroductionAccurate prediction of layer thicknesses from sur-
face seismic data away from well control is importantfor volumetric calculations, reservoir characterization,well planning, well steering, and identification of addi-tional drilling opportunities. This is particularly chal-lenging for thin layers as defined by Widess (1973).In this case, the interpreted or apparent time thick-ness/isochron of the thin layer is not representativeof the actual time thickness of the layer, which meansthat the apparent thickness can be misleading. For athin layer, the amplitude of the associated compositereflection event is not only dependent on the imped-ance contrast but also on the actual layer thickness(Widess, 1973; Kallweit and Wood, 1982) and providesan avenue for thickness estimation.
The amplitude variation with thickness, or tuningcurve, is largely dependent on the wavelet embeddedon the seismic data (Brown et al., 1986) as well asthe sign and symmetry of the reflection coefficients(RFC). Practitioners have deployed several approachesto mitigate or decouple the effect of the wavelet fromthe analysis of seismic data at a particular target, suchas spectral decomposition (Okaya, 1995; Partyka et al.,1999), spectral inversion (Puryear and Castagna, 2008),spectral shaping techniques such as colored inversion(Lancaster and Whitcombe, 2000), and spectral blueing(Neep, 2007). These approaches aim to reduce the tun-ing effect on subsets of the seismic volume, so the in-terpreter can map seismic events associated to top and
base of the target bed and extract amplitude and trav-eltime information more directly associated with thelayer properties.
Brown et al. (1984, 1986) combine mapping top andbase event information with a modeled tuning curve toobtain thickness estimates for thin and thick layersbased on reflectivity data. In their method, an envelopewas fit to the scattered points on the composite ampli-tude versus isochron crossplot. This envelope repre-sents the reflectivity of a clean sand with a 100% netto gross (NTG) and defines the tuning curve that couldbe calibrated using wellbore information, if well dataare available. The method also relies on the assumptionthat for any apparent thickness value, the compositeamplitude to tuning curve ratio would be proportionalto the NTG.
Connolly (2007) proposes a similar map-based ap-proach using a relative-impedance (after colored inver-sion) volume, instead of a reflectivity volume, as thebasis for mapping the top and base horizons of the tar-get layer (zero-crossings). This is known as the seismicnet-pay (SNP) method. The “detuning” approach is alsovery similar to that in the reflectivity domain, resultingin calibrated relative-impedance values and reducedtuning imprint on the outcome. The technique is basedon the following assumptions:
• The section of interest is represented by a suiteof constant-impedance reservoir layers embeddedin a matrix of constant-impedance nonreservoirrocks.
1Quantitative Interpretation Team, BHP Billiton Petroleum, Houston, Texas, USA. E-mail: [email protected]; [email protected]; [email protected].
2University of Houston, Department of Earth and Atmospheric Sciences, Houston, Texas, USA. E-mail: [email protected] received by the Editor 31 October 2014; revised manuscript received 29 January 2015; published online 15 April 2015. This paper
appears in Interpretation, Vol. 3, No. 2 (May 2015); p. B25–B36, 19 FIGS.http://dx.doi.org/10.1190/INT-2014-0241.1. © 2015 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.
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• The apparent thickness is less than one half-cycle of the lowest frequency component of thewavelet.
• The reservoir must be seismically isolated.
Once these assumptions are met, then the averagerelative impedance for a given apparent thickness isproportional to the seismic NTG (true net pay dividedby apparent thickness). This variable is obtained fromthe average relative impedance versus apparent iso-chron chart in a similar way to that of the reflectiv-ity-based method mentioned earlier (Brown et al.,1984, 1986).
The SNP method’s binary impedance requirementcan be achieved using extended elastic impedance(EEI) (Whitcombe et al., 2002), which is in essence acoordinate rotation of the acoustic impedance (AI)and gradient impedance (GI) volumes obtained fromcolored inversion (Connolly, 1999) of the amplitude-versus-angle (AVA) intercept and gradient-amplitudedata, respectively. On relative-impedance domain, thisAI-GI coordinate rotation has the following form:
LnEEIðχÞ ≈ LnAI · cosðχÞ þ LnGI · sinðχÞ. (1)
This AI-GI coordinate rotation is controlled by theangle χ, which is selected to optimize the discriminationof a particular reservoir property, such as lithology(sand versus shales) or fluid content (hydrocarbon ver-sus brine) in a section of interest (Whitcombe andFletcher, 2001). The SNP method would yield net sandor net pay outcomes depending on the property that theinput EEI volume has been optimized for, either lithol-ogy or fluid, respectively.
Simm (2009) makes a comparison of reflectivity-based and SNP methods using modeled data consisting
of layers of sand with varying thickness embedded in ashale background. He generates the characteristic seis-mic response of geologically plausible layering withdifferent NTG values. He finds that the SNP methodyields more accurate net-pay predictions than the re-flectivity-based method, provided that the rock andfluid properties do not deviate from the method’s as-sumptions above.
The objective of this case study is to assess whetherthe technical effort associated with yielding thicknessestimates via SNP may be justified by obtaining moreaccurate and reliable net pay/sand predictions in explo-ration scenarios as claimed by Connolly (2007) andSimm (2009). The technical effort required for carryingout the SNP method consists mainly of performing col-ored inversions of prestack data, selecting the optimalrelative-impedance volume for either net sand or netpay based on EEI, and the accurate horizon pickingof top and base of the layer of interest on the zero cross-ings of the corresponding relative-impedance volume.
The accuracy of the SNP method was compared withconventional tuning analysis by evaluating the predic-tion accuracy of both methods using the actual netpay penetrated in the wells, under the assumptionsstated above.
MethodologySeismic analysis and rock physics
The hypothesis that the SNP method could accu-rately determine layer thickness from a measured seis-mic response was tested using data from an oil fieldlocated on the northwest shelf of offshore Australia.The reservoir target was a Cretaceous high-porosity,low-impedance, deep marine turbidite sandstone em-bedded in a thick, high-impedance, and somewhathomogeneous deepwater shale section. Normal faultingis the dominant structural style of the hydrocarbontraps, and the vast majority of the drilling targets inthe area for exploration and development were identi-fied using anomalous seismic amplitude measurementsinterpreted as direct hydrocarbon indicators. The inter-preted depth horizon for the top of the reservoir layer isshown in Figure 1, overlaid by seven production wells(shown as black stars) with complete penetration of thereservoir.
Semiregional rock-physics analysis (Duncan et al.,2013) has established that the presence of hydrocar-bon-bearing sands results in class III AVA behavior(Rutherford and Williams, 1989; Castagna and Swan,1997). Forward modeling indicates that the AVA gra-dient does not significantly change with fluid contentfor given lithology and porosity conditions, which areassumed to be fairly uniform in the area. The AVA in-tercept is significantly sensitive to the pore fluid changeas observed on the half-space modeling in Figure 2.
Six exploratory wells were used for regional rock-physics analysis, in addition to the seven productionwells within the oil field. The production wells allhad a complete penetration of the target bed for SNP
Figure 1. Interpreted horizon for the top of the reservoir tar-get layer in depth, overlaid by production wells with completepenetration on the reservoir (black stars).
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validation purposes. Overall good-quality, AVA-compli-ant prestack time-migrated (PSTM) seismic data setswere used in this study, along with a complete suiteof wireline wellbore logs, which were used for perform-ing well-seismic ties, such as the example shown in Fig-ure 3. This example is representative of data quality andsemiregional framework, depicting an isolated high-NTG sand as the target bed (yellow arrow) with easilyidentified top and base reflections at or below tuning.
The PSTM processing on the data set included post-migration coherent noise attenuation, spectral balancing,and residual moveout (RMO), producing angle gathersthat yielded four angle stacks whose central anglesare 8°, 18°, 26°, and 33°. Gathers were not available atthe time of this study; therefore, pseudogathers wereconstructed by resorting the traces of each angle stackinto a common depth point (CDP) gather ensemble offour fold, with each trace corresponding to the centralangle cited above. Figure 4 shows a vertical cross sectioncontaining these pseudogathers displayed every fifthCDP, with the central angle of each trace going from leftto right on each pseudogather. Overlying a collocatedgather is the zero-offset synthetic trace previously shownin Figure 3. The seismic events corresponding to the tar-get layer are enclosed by the green polygon.
The AVA intercept and gradient stacks were gener-ated from the pseudogathers, and then tied and coloredinverted using an elastic-impedance approach (Con-nolly, 1999) to design a global inversionoperator per each stack as shown in Fig-ure 5. The figure shows the amplitudespectra of the AI logs from the sevensemiregional wells and the linear fit totheir mean. This fit represents the targetspectra for designing the colored inver-sion operator used to shape the inter-cept stack to match the band-limitedAI observed in the wellbore data. Thesame procedure was performed to de-sign the corresponding operator forband-limited GI. These steps aim atobtaining AI and GI volumes.
Rock-physics modeling suggestedthat there will be no χ angle capableof yielding a clear EEI volume for lithol-ogy discrimination; but a EEIðχ ∼ 25°Þvolume was expected to enhance thediscrimination between hydrocarbon-bearing and brine-bearing sands, whileminimizing the sensitivity to porosity.Figure 6 shows the same well log asshown in Figure 3, this time depictingthe logs for AI, GI, and EEIðχ ¼ 25°Þ, fil-tered using a trapezoidal filter 10/18–85\100 Hz to match the overall bandwidthof the seismic data. The band-limitedlogs show that the EEIðχ ¼ 25°Þ volumeshould improve the illumination of thepay layer. However, the corresponding
collocated seismic traces for AI, GI, and EEIðχ ¼25°Þ, show that seismically EEIðχ ¼ 25°Þ does not im-prove the illumination of the pay layer sufficiently thatit can be interpreted as a unique event in the windowof interest. Further χ angle scanning was performedwith no improvement on the pay illumination when
Figure 2. Modeled AVA half-space response for a shale over-lying brine-saturated (blue) and oil-saturated (green) reser-voir sandstone (after Duncan et al., 2013).
Figure 3. (a) Semiregional wellbore S-1 depicting pertinent logs (shale volumeV sh, water saturation Sw, total porosity PHIT, P-wave velocity VP, bulk densityrhob, AI, and reflection coefficients RC), zero-offset synthetic trace (blue), andcollocated actual near-angle stack trace (red). The polarity convention is SEGnormal. The target sand is highlighted by the yellow arrow. (b) Wavelet for welltie in the time domain, wavelet spectrum, and crosscorrelation function indicat-ing a correlation coefficient higher than 80% and no apparent time shift.
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compared with the AI volume (AI ≡ EEIðχ ¼ 0°Þ). Thisraised uncertainty about the quality of the AVA gradientas derived from the pseudogathers. Because of the lackof a good lithology projection and uncertainty in thequality of the AVO gradient, it was decided to performthe SNP exercise using the AI volume.
Seismic net-pay estimationConnolly (2007) performs three 1D forward models
that hold sand and shale elastic properties constant,only varying the total net sand thickness among themodels. In this study, we use the same net sand relativedistribution to build wedge models as seen in Figure 7,
to obtain a first-hand understanding of the actual imple-mentation of the method under controlled conditions.In this modeling case, the total true net sand thicknesschanges with true gross thickness, but the models werebuilt in such way that the true NTG is held constant.Model A (top) has NTG ¼ 1, model B (middle) hasfour sands that combined yield a NTG ¼ 0.57, andmodel C (bottom) has two sands that combined yielda NTG ¼ 0.47.
The top and base horizons can be used to calculatethe average relative impedance and apparent timethickness for each model (Figure 8). The average rela-tive impedance is proportional to the underlying NTG
Figure 4. Vertical section of AVA pseudogathers, displayed every fifth CDP. Incidence angle (8°, 18°, 26°, and 33°) increases fromleft to right on each pseudogather. Collocated zero-offset synthetic trace of well S-1 is shown in red. Seismic events associated totarget layer are enclosed by the green polygon. The data polarity convention is SEG normal.
Figure 5. (a) Spectral response of the AI logsfor all the semiregional wells over the windowof interest and (b) mean impedance spectralresponse, target for colored-inversion opera-tor design.
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for any given time thickness up to 25–30 ms, in agree-ment with the SNP method assumption related to themaximum thickness for which this proportionality isclearly met, in this case approximately 27.5 ms (one-half wavelength of the lowest usable frequency on data,which is approximately 18 Hz).
The SNP method takes advantageof this relationship between relativeimpedance and NTG to estimate net-pay thickness for varying true NTG. Ifit is assumed that the envelope of scat-tered points on the relative impedance-apparent thickness plane represents theresponse of an expected maximum NTGof one, relative-impedance values be-tween that envelope and zero will pro-vide an estimate of the actual NTG.Seismic NTG for wedge models A, B,and C are shown in Figure 9, illustratingconvergence to the true NTG beyondtuning thickness. The seismic NTG be-comes considerably lower as the appar-ent time thickness becomes lower thanthe tuning thickness. By interpretingFigures 8 and 9, it can be concluded thatthe net-pay estimation must include aproportionality or scaling function to beapplied to the apparent thickness thatdepends on the average relative imped-ance (Connolly, 2007; Simm, 2009).
Such a correction or scaling functionis obtained from wedge modeling for amaximum NTG:
corrðΔtÞ ¼ modeled seismicNTGðΔtÞmodeledAIðΔtÞ . (2)
The correction function is dependent on the waveletand is approximately linear, as depicted in Figure 10for model A. This function allows obtaining an estima-tion of net pay for thin and thick layers based on Simm’s(2009) proposed SNP formula:
SNPðx; yÞ ¼ corrðΔtÞ · AIðx; yÞ · Δzðx; yÞ. (3)
For each mapped ðx; yÞ point on the seismic survey,the SNP will depend on the arithmetic product of thecorrection factor corr for the apparent time thicknessΔt at ðx; yÞ, the corresponding average relative imped-ance AI and the apparent isopach Δz at the sameðx; yÞ pair.
If this scaling function based on wedge model A canbe calculated and calibrated to closely match predictedversus actual net-pay thickness, then this correctionfunction for maximum seismic NTG can also be appliedto models B and C to verify the accuracy of the SNPmethod for varying true NTG. Figure 11 depicts pre-dicted (y-axis) versus actual (x-axis) net-pay thicknessfor models A (top), B (middle), and C (bottom). Pre-
dicted values closely match the actual ones for modelsA and B with reduction of accuracy for larger gross in-tervals. In the case of model C, the accuracy is alsoseverely affected by the internal layering of the pay,which in this case becomes less evenly distributed
Figure 6. The same well as shown in Figure 3, this time including band-limitedlogs and the respective collocated seismic traces for AI, GI, and EEIðχ ¼ 25°Þ.The target pay layer is highlighted by the yellow arrow.
Figure 7. Wedge models. Model A NTG ¼ 1, model BNTG ¼ 0.57, and model C NTG ¼ 0.47. The background graycolor represents higher impedance shales, in yellow the lowerimpedance sandstones, and the overlying traces are the mod-eled band-limited impedance traces for the wedges. The redand blue horizons represent the apparent top and base of thewedges as interpreted on the zero crossing of the band-limitedimpedance traces.
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within the apparent gross interval (Connolly and Kem-per, 2007).
The SNP method requires accurate interpretation oftop and base horizons of the target layer on the AI vol-ume at the corresponding zero crossing as seen in Fig-ure 12. These horizons form the basis for the input ofthe SNP method because they will be used to extractthe average relative impedance, apparent isochron,and isopach maps (using an interval velocity volume),which are the basis of the impedance tuning curve forseismic NTG, as defined by Connolly (2007) and as de-picted in Figure 13. Similarly to the reflectivity-basedmethod, a relative-impedance tuning curve is obtainedfrom a wedge model based on a trapezoidal filter rep-resentative of the AI volume bandwidth and scaled tothe envelope of the scattered points on the average rel-ative impedance-isochron plot. This tuning curve will
Figure 8. Average band-limited impedance versus apparenttime thickness for model A (red), model B (cyan), and modelC (green).
Figure 9. Seismic NTG (NTGs) versus apparent time thick-ness for model A (red), model B (cyan), and model C (green).
Figure 10. Correction or scaling factor versus apparent timethickness for model A.
Figure 11. Predicted (y-axis) versus actual (x-axis) net-payvalues (ms) for models A (top), B (middle), and C (bottom)based on calibration of model A.
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represent the maximum seismic NTG at each apparenttime thickness value.
For any kth point on a crossplot such as the oneshown in Figure 13, the main component of the detun-ing is the calculation of its seismic NTG:
seismicNTGk ¼AIk
BðΔtkÞ; (4)
where AIk is the measured relative AI and BðΔtkÞ is therelative-impedance value of the modeled tuning curveevaluated at Δtk. Once the seismic NTG is calculated,then it undergoes the application of the apparent-thick-ness correction factor obtained from the modeled tun-ing curve.
In cases in which well data are available, the calibra-tion process is basically controlled by adjusting thescaling factor of the modeled tuning curve, such thatthe AIk∕B ratio, as shown in Figure 13, becomes propor-tionately closer to the actual NTG at the well.
The general workflow for SNP based on Connolly(2007) and Simm (2009) is depicted in Figure 14. Sev-eral of the steps can be easily performed on modernworkstation and commercial software applications al-ready available for interpreters and seismic analysts.
ResultsThe SNP thickness estimates were validated using
the actual net pay measured at seven wellbores thatpenetrated the target bed located within the oil-leg ofthe reservoir. The first validation scenario assumed thatno well calibration to actual thickness has been per-formed during the detuning process on SNP, definedby Connolly (2007) as self calibration. Where there isno local well control, such as in near-field explorationor early field appraisal projects, the application of theSNP method can have a big impact on the net-pay es-timation. Figure 15 shows such a validation scenario:the average AI-apparent isochron plot (left) depictsthe scattering of points as plotted from the input attrib-ute horizons. A modeled tuning curve for impedancewas created with the main controlling parameters beingthe wavelet bandwidth (a 10/18–85\100 Hz trapezoidalfilter) and a wavelet scaling factor; the latter is adjustedin such a way that the modeled tuning curve representsan overall envelope to the scattered data points andalso the maximum seismic NTG ¼ 1. Also in this plot,the validation wells are plotted based on their inputattribute values. The predicted versus actual net pay(right) provides a graphical insight of the quality ofthe prediction at well locations. The y-axis error barswere defined by the SNP range around each wellboresneighboring CDPs (in this case, eight neighbor CDPbins). The x-axis error bars were defined by the net-pay error obtained from the petrophysical evaluation.Neither over- nor underprediction of significance is ob-served, so it is reasonable to state that the overall rel-ative net-pay estimation is capturing the actual net-payvalues. It can be observed that large y-axis error bars
are present in some cases and some are not interceptedby the perfect fit line as expected, which implies thatsome flaws on the current deployment might be affect-ing the quality of the predictions. For self-calibration,the mean absolute prediction error at well validationlocations is under 3.0� 1.5 m. The parameterizationon this self-calibration tuning curve is then used to
Figure 12. Seismic section (west–east) of relative AI withsemiregional S-1 well and major bounding faults overlaid. Tar-get layer is highlighted by the yellow arrow and embedded bythe interpreted top (green) and base (magenta) horizons.
Figure 13. Schematic chart depicting the main input ele-ments involved on the SNP method: Modeled band-limitedimpedance wedge model tuning curve for maximum NTG(dashed cyan), apparent thickness, and average band-limitedimpedance maps as obtained from the horizon picking proc-ess at the target event on the band-limited impedance volume.
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yield seismic NTG and SNP maps from the input iso-chron, isopach, and average relative-impedance mapsas shown in Figure 16.
Comparative net-pay estimationsThe SNP method combines two attributes (isochron
and average relative impedance) and yields an outcomethat correlates with the actual net-pay values observedat the wellbores. However, the level of correlation is notnecessarily diagnostic of the reliability of the predictiongiven that a spurious correlation may exist (Kalkomey,1997). In other words, the predicted net-pay values us-ing the SNP method may correlate with the actual net-pay values not because there is an underlying causalrelationship but because a correlation occurs just bychance. To estimate the spuriousness of the SNP out-comes, a statistical significance test known as the F -test(McKillup and Darby, 2010) was performed to assessthe spuriousness of the net-pay predictions, accordingto the following equation:
F ¼hR2
K
ih
1−R2
n−K−1
i ; (5)
where K represents the number of predictors, n repre-sents the number of data points, and R is the correlationcoefficient of the linear fit between the observed andpredicted values as yielded by the combined predictors.In this case, K is the number of seismic attributes usedfor prediction of net pay and n is the number of wellspredicted. The larger the value of F , the more likely thatthe results are statistically significant. As a rule ofthumb in such studies, as F falls below one, we loseconfidence in the predictive ability of the method.
To assess the value of the SNP predictions, webenchmark against two standard methods: reflectivitydetuning and multivariable linear regression. The multi-variable linear regression uses the same attributes (iso-chron and average relative impedance), calibrated withall wells other than the predicted well. The multivari-
able linear regression is an empiricalmethod that ignores tuning effects andhas been occasionally used by inter-preters and geomodelers. The measuredΔt and AI at each calibration well wereused to determine the multivariable lin-ear regression coefficients that thenwere used to predict the correspondingnet-pay values for the out-of-samplewells. This crossvalidation involves pre-dicting at each well location using theother six wells to calibrate the regres-sion coefficients. So each prediction ateach well is a result of a slightly differ-ent regression equations. For thismethod, the mean absolute predictionerror at well validation locationsis 5.9� 5.8 m.
Figure 14. General workflow for SNP (modified after Con-nolly, 2007; Simm, 2009).
Figure 15. (a) Self-calibration tuning chart and (b) predicted versus actual netpay at each validation wellbore location.
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Figure 17 shows the comparison of two statisticsmetrics of this benchmark. Figure 17a depicts the cor-relation coefficient R2 yielded from the linear fit amongpredicted and actual net-pay values using the SNP (bluebars) and multivariable linear regression (red bars).Figure 17b shows the F -test outcomes for the samemethods. For the multivariable regression, six wellsare used for calibration and the results are reportedfor the calibration wells and an additional out-of-samplewell. The SNP method consistently yields a very highcorrelation coefficient R2 of approximately 0.7–0.8;the correlation coefficient for the multivariable linearregression method is comparable, approximately 0.6.However, the F -test outcomes for the multivariable lin-ear regression method are significantly lower, almosthalf of those of the SNP method, indicating thatthe SNP method, for this case study, yields net-pay pre-dictions that are close to the actual net-pay values. Fur-thermore, the accuracy of the predictions is statisticallysignificant.
Simm (2009) performs a modeling study comparingthe SNP method and reflectivity (RFC)-based method(Brown et al., 1984, 1986). In our study, we comparethe two methods based on actual seismic and well dataonly for a self-calibration scenario to assess the value ofperforming SNP instead of reflectivity-based estima-tions in exploration scenarios. Figure 18 shows thecomposite amplitude on the y-axis versus the isochronon the x-axis as extracted from reflectivity or amplitudedata, in this case, the near-angle stack reflectivity. Sim-
ilar to the SNP method, self-calibration is performed bybuilding a tuning curve (red) from wedge modeling as-sumed to represent a maximum NTG of one and scalingit to represent the envelope of the scattered points. Am-plitude detuning is performed by applying an isochron-dependent scalar that is a function of the ratio of the no-tuning baseline (blue) and the maximum NTG tuningcurve (red). The amplitude-based net-pay estimate(RFC) is obtained by multiplying the detuned amplitudemap by the apparent thickness map. Figure 19 showsthe predicted versus actual net-pay values of the SNP(Figure 19a), RFC (Figure 19b), and multivariable re-gression (Figure 19c) methods. No wells (i.e., self-cali-bration) were used for the SNP and RFC methods,whereas six wells were used to calibrate the multivari-able regression method. The results show that the RFC-based method, in this case, underestimates the net-payvalues as compared with the SNP outcome (the meanabsolute prediction error at well validation locations is4.0� 2.7 m for the RFC-based method), despite the factthat both methods perform a detuning of the data byapplying isochron-dependent scalars derived from for-ward-modeled data. The differences in the methods aredue to (1) an inherent better detuning on relative imped-ance compared with the reflectivity volume and (2) themodeled tuning curve on the reflectivity-based methodcontains several peaks/valleys thataffect thepredictionofvalues above tuning thickness (Simm, 2009). When wellcalibration is available, a further linear fit between netpay at the wells and the predicted net-pay estimates
Figure 16. (a) Apparent isochron, (b) apparent isopach, (c) average relative impedance input maps for the SNP method. (d) Self-calibration seismic NTG, and (e) net pay output maps. Validation wells are represented by black stars.
Figure 17. (a) Square of correlation coeffi-cient for the SNPmethod (blue) and multivari-able linear regression (red) and (b) F -test forthe SNP method (blue) and multivariable lin-ear regression (red).
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can be used to remove bias or systematic error in the re-sults and improve accuracy. The SNP method is appa-rently unbiased without well calibration: The only biasof SNP on this self-calibration mode would consist ofthe selection of the scaling factor applied to the modeledtuning curve. In contrast, themultivariable regression haswide scatter (mean error is 5.9� 5.8 m) due to the non-linearity of convolution with a wavelet for thin layers.
DiscussionDocumented in this paper is the application of SNP
technology (Connolly, 2007) to a geologic setting thatclosely adheres to the assumptions of the method.The more general inference from the results is thatthe method may yield predicted values that are closeto the actual net-pay values, even without well calibra-tion, which is of the utmost importance in exploration.
However, there is still a substantial uncertainty thatneeds to be recognized in subsurface management de-cision making. It is important to highlight the possiblesource(s) of uncertainty of the predictions, which forthis particular study were identified as (1) accuracyin horizon picking, (2) limited-frequency bandwidthof the seismic data and in particular deficiency in thelow-end frequencies, and (3) lateral variations in bothfluid properties and porosity.
Horizon picking accuracyEven though picking zero crossings on a relative-
impedance volume is significantly easier than pickingthe corresponding amplitude onsets on a reflectivityvolume, it is still probable that inaccurate picks mayarise. This is especially the case when working witha large volume of data that demand the intensive usageof state-of-the-art automated picking tools, which wasthe case in this project. In our experience with otheroffshore settings, it has been found that for deepwatersands visible on seismic data, the base reservoir reflec-tion is commonly easier to pick and to extend survey-wide than the top reservoir reflection because the tar-get layer commonly shows a significant impedance con-trast at the base. These kind of reservoir sandstonesusually have a fining-upward component or even minorscour surfaces on top that affect the strength andcontinuity of the top reflector. The horizon picking in-accuracy may affect the method in two ways. First, in-accuracy in the apparent isochron and isopach mapscalculated from the top and base horizons give riseto inaccurate apparent thickness maps. Second, inac-curacy in the average band-limited impedance changesthe number of samples contributing to the mean thatmust be calculated between the top and base of thelayer represented by the horizons picked on the zerocrossings. To the best of our knowledge, the methoddoes not explicitly account for this horizon picking in-accuracy, which may be difficult to estimate given thenonobvious deterministic nature of it.
Figure 18. Composite amplitude versus apparent thicknessas measured from reflectivity data (black points), includingoverlay of collocated well observations (green circles), wedgemodel clean sand curve (red), and no-tuning baseline (blue).
Figure 19. (a) Predicted versus actual net-pay thickness at validation locations using the SNP method, (b) the reflectivity-basedmethod, and (c) multivariable linear regression. The SNP method mean error is 3.0 m, and its standard deviation is 1.5 m. Thereflectivity-based method mean error is 4.0 m, and its standard deviation is 2.7 m. The multivariable linear regression method meanerror is 5.9� 5.8 m.
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Deficiency of low-end frequenciesBy design, colored inversion shapes the average input
seismic spectra tomatch that of the average band-limitedimpedance log (Lancaster and Whitcombe, 2000), whichyields two very attractive output characteristics: (1)global matching of the impedance magnitudes betweeninverted seismic and impedance logs in a band-limitedbasis and (2) spectral shaping carried out by the coloredinversion operator yields an outcome with boosted low-end frequencies compared with the high-end frequencieswithin the input seismic bandwidth, leading to a consid-erable attenuation of the side lobes with the subsequentreduction of the tuning effect.
It is important to highlight the fact that our data setbandwidth at target was approximately 20–85 Hz, highlydeficient in low-end frequencies that are known to sig-nificantly drive the detectability of events from seismicdata. Kallweit and Wood (1982) perform forward mod-eling using two wavelets with equal high-end but differ-ent low-end frequencies, and show that the resolutiongraph (apparent versus actual time thickness) for bothwavelets remained almost unchanged as a function ofthe low-end frequency. In contrast, the detection (tun-ing) chart for the broader-bandwidth wavelet was quitesensitive to the low-end frequencies, with the waveletside lobes attenuated when low-end frequencies areadded. Despite the good results obtained from SNP,the deficiency in the low-end frequency bandwidthon our data set may limit the ability of achieving a moreefficient detuning of the input maps, adversely affectingthe SNP outcomes (Connolly and Kemper, 2007).
Lateral changes in fluid phase and porosityWellbores S-3 and S-6 were consistent outliers for the
SNP predictions. Though not explicitly depicted in thispaper, these two wellbores lie very close to the oil-water contact (OWC), where lateral variations on watersaturation may be of enough magnitude to producemore transitional rather than abrupt seismic responsesassociated with the presence of the fluid contact withinthe seismic resolution limits. In that case, althoughthese wellbores lie within the hydrocarbon imprint ofthe field, they may not be totally correlated with the restof the wellbores in terms of fluid saturations. The deter-mination of a good EEI lithology projection can be usedto avoid the effect of lateral changes in fluids; however,in this case, there was not a good lithology projection.
A similar problem may arise when dealing with res-ervoirs with significant lateral variations in porosity be-cause the SNP and RFC detuning methods assumeconstant properties for the reservoir. This emphasizesthe importance of using a reliable EEI volume opti-mized for fluid illumination and able to minimize thesensitivity to porosity variations. Also, the petrophysicalcut-offs for clay content, porosity, and water saturationshould be used to define the range of velocity variationfor reservoir sands, and these velocity ranges should beused to establish the range of uncertainty in the tuningcurve, reservoir thickness, and scaling factor.
ConclusionsWe have tested the SNP method on a data set that we
claim complies with the assumptions of the method. TheSNP method captures the relative net-pay trends as va-lidated by wellbore data, especially when no well infor-mation is used for calibration of net-pay estimations,which is very important in exploration scenarios wherewell control is scarce or inexistent. The SNP methodyielded a mean absolute error of 3.0� 1.5 m. In contrast,the multivariable linear regression method yielded an er-ror of 5.9� 5.8 m and the reflectivity-based detuningmethod yielded an error of 4.0 � 2.7 m. In terms of stat-istical significance of the predictions, the SNP methodyielded an F value of approximately 5.8, whereas theF value for multivariable linear regression methodwas less than half (approximately 2.8), indicating fewerstatistically significant results compared with the self-calibrated SNP outcomes. When fewer wells were usedto calibrate the multiple regression results, the expectedstatistical significance was poorer. The reflectivity-basedmethod yielded a robust statistical significance of 5.5, inrange with that of the SNP method, but it is more biasedwithout well calibration.
The main strength of the SNP method is the use of aband-limited calibrated impedance volume obtainedfrom colored inversion that scales and partially detunesthe input amplitude data. However, the method may bevery sensitive to other variables beyond data qualityand rock-physics assumptions, such as accuracy ofthe horizon interpretation. Although we did not testthe method in other geologic settings, the assumptionsof the method may restrict its deployment to a variety ofexploration settings. For instance, thick stacked sandpackages can cause seismic interference that canclearly violate the requirement of seismic isolation ofthe layer under study. The method may be also heavilyrestricted on some fluvial and transitional environ-ments where the binary impedance assumption maynot necessarily apply.
Even though the SNP and the reflectivity-basedmethods are map-based detuning techniques, in the ab-sence of well calibration SNP seems to provide moreaccurate predictions than the reflectivity-based method.However, further analysis may be needed to quantifyand compare the impact of each method on any subsur-face management decision making. In any case, bothmethods show the importance of considering tuning ef-fects when considering amplitude strength for net-sandand net-pay estimation.
AcknowledgmentsThe authors wish to thank BHP Billiton Petroleum
and Woodside Energy Ltd. for permission to publishthese results. We are particularly thankful to our col-leagues G. Duncan, R. Hill, R. Keen, M. Gutierrez, B.Asher, S. Misra, and S. Tadepalli for their accurate in-sights, support, and information during the project.
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Kalkomey, C. T., 1997, Potential risks when using seismicattributes as predictors of reservoir properties: TheLeading Edge, 16, 247–251, doi: 10.1190/1.1437610.
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Lancaster, S., and D. Whitcombe, 2000, Fast-track ‘colored’inversion: 70th Annual International Meeting, SEG, Ex-panded Abstracts, 1572–1575.
McKillup, S., and M. Darby, 2010, Geostatistics explained:An introductory guide for earth scientists: CambridgeUniversity Press.
Neep, J. P., 2007, Time variant colored inversion and spec-tral blueing: 69th Annual International Conference andExhibition, EAGE, Extended Abstracts, B009.
Okaya, D. A., 1995, Spectral properties of the earth’s con-tribution to seismic resolution: Geophysics, 60, 241–251, doi: 10.1190/1.1443752.
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Rutherford, S. R., and R. H. Williams, 1989, Amplitude-ver-sus-offset variations in gas sands: Geophysics, 54, 680–688, doi: 10.1190/1.1442696.
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Ramses G. Meza received a geo-physical engineering degree fromthe Universidad Simon Bolivar, Ven-ezuela, an M.S. in geophysics fromthe Colorado School of Mines, USA,and he is pursuing a Ph.D. in geophys-ics from the University of Houston. Heis a geophysicist with BHP BillitonPetroleum. Early in his career, he
worked as a reservoir geophysicist at PDVSA in PuertoLa Cruz, Venezuela, Harvest-Vinccler in Maturin, Ven-ezuela, and ConocoPhillips in Houston, USA. His respon-sibilities included support in terms of quantitative seismicinterpretation for hydrocarbons exploration and produc-tion activities. Since 2012, he has been with the BHP Billi-ton’s quantitative interpretation (QI) team providingsupport to all E&P assets with emphasis on integrationof QI products, visualization, seismic attributes and visuali-zation, QI quality assurance, seismic reservoir characteri-zation, DHI analysis, and risking. He is a member of SEG,AAPG, EAGE, and SOVG.
Juan Mauricio Florez received aB.S. in geology from the Universityof Nacional of Columbia and a Ph.D.in geophysics from Stanford Univer-sity. He is a geophysicist at BHP Billi-ton Petroleum, currently manager ofthe QI team. He worked as an explo-ration geologist in Colombia for abouteight years, and after finishing his
graduate studies in 2005, he has worked in reservoir char-acterization (iReservoir), rock physics, and AVO modeling(BP America) and more recently quantitative seismic inter-pretation with BHP Billiton.
Stanislav Kuzmin received a Ph.D.(2004) in physics from the Universityof California, and he worked on a va-riety of exploration and developmentprojects in different basins. He is ageophysicist at BHP Billiton Petro-leum, and the main emphasis of hiswork is QI and rock physics.
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