Integration of broadband seismic data into reservoir characterizationworkflows: A case study from the Campos Basin, Brazil

Ekaterina Kneller1 and Manuel Peiro1

Abstract

Towed-streamer marine broadband data have been key contributors to recent petroleum exploration history,in new frontiers and in mature basins around the world. They have improved the characterization of reservoirsby reducing the uncertainty in structural and stratigraphic interpretation and by providing more quantitativeestimates of reservoir properties. Dedicated acquisition, processing, and quality control (QC) methods havebeen developed to capitalize on the broad bandwidth of the data and allow their rapid integration into reservoirmodels. Using a variable-depth steamer data set acquired in the Campos Basin, Brazil, we determine that par-ticular care that should be taken when processing and inverting broadband data to realize their full potential forreservoir interpretation and uncertainty management in the reservoir model. In particular, we determine the QCimplemented and interpretative processing approach used to monitor data improvements during processing andpreconditioning for elastic inversion. In addition, we evaluate the importance of properly modeling the lowfrequencies during wavelet estimation. We find the benefits of carefully processed broadband data for structuralinterpretation and describe the application of acoustic and elastic inversions cascaded with Bayesian lithofaciesclassification, to provide clear interpretative products with which we were able to demonstrate a reduction inthe uncertainty of the prediction and characterization of Santonian oil sandstones in the Campos Basin.

IntroductionThe Campos Basin is located offshore Southeast Bra-

zil and covers 115;000 km2 along the northern coast ofRio de Janeiro state and the southern coast of EspiritoSanto state. The Campos Basin is historically the mostprolific Brazilian basin, accounting for 74% of Brazilianoil and 32% of national gas production in 2015 (Bastos,2015). Exploration in the Campos Basin started in thelate 1950s, and shallow-water oilfields were discoveredin the 1970s. Deepwater fields, with turbidite reservoirsat various chronostratigraphic levels, were discovered inthe 1980s and 1990s, and we are now in a phase of ultra-deepwater discoveries. The Xelerete concession, located250 km off the coast of the Rio de Janeiro State, is onesuch discovery. The field, containing heavy oil, was dis-covered in 2001 in a water depth of 2400 m. Although apresalt prospect was later identified below the main clas-tic target, this paper focuses on the first postsalt discov-ery that was declared commercial in 2007 with, at thetime, an estimated in-place volume of 1.4 billion boe(source: Petrobras press release, 09/10/2007).

Post-salt sandstones in the Campos Basin are char-acterized by complex depositional systems, showinghigh degrees of reworking and redistribution (Muttiand Carminatti, 2012) as shown in the schematic crosssection in Figure 1. These systems are seismically

highly anisotropic and heterogeneous, with sharplyvarying thicknesses and some volcanic intrusions.The geologic setting of the Campos Basin makes it chal-lenging for seismic imaging, reservoir characterization,facies identification, and ultimately reservoir uncer-tainty management. Nevertheless, post- and presaltdiscoveries in the deepwater part of the basin have sus-tained the continuous demand for seismic explorationin the area.

In response to those challenges, CGG acquired abroadband multiclient seismic survey in the CamposBasin in 2014, covering an area of approximately10,000 km2. Being rich in low- and high-frequency con-tents, broadband data can image deeper targets, im-prove vertical resolution, and ultimately reduce theuncertainty in reservoir characterization. The benefitsof such an acquisition configuration have been demon-strated in previous seismic imaging (Soubaras andWhiting, 2011; Masclet et al., 2015a) and seismic veloc-ity model building work (Masclet et al., 2015b). The im-provement in reservoir characterization conditioned bythe wider frequency bandwidth of broadband seismicdata has also been shown in previous case studies (La-fet et al., 2012; Reiser et al., 2012; Soubaras et al., 2012;Wallick and Giroldi, 2013; Kneller et al., 2013; and Mi-chel and Sablon, 2016).

1CGG, GeoConsulting, Rio de Janeiro, Brazil. E-mail: ekaterina.kneller@cgg.com; manuel.peiro@cgg.com.Manuscript received by the Editor 7 March 2017; revised manuscript received 12 August 2017; published ahead of production 08 November 2017;

published online 21 December 2017. This paper appears in Interpretation, Vol. 6, No. 1 (February 2018); p. T145–T161, 30 FIGS.http://dx.doi.org/10.1190/INT-2017-0049.1. © 2018 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.

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Technical papers

Interpretation / February 2018 T145Interpretation / February 2018 T145

As for any type of seismic data, a fundamental prin-ciple remains true for broadband data: Reservoir char-acterization will only fully benefit from the seismic dataif it is properly processed and imaged. This is achievedthrough new methods and algorithms that are bettersuited to the wider bandwidth, and, in particular, thelow-frequency content of the data (Mesdag and Scha-kel, 2015; Soubaras, 2016) as well as the judiciouschoice of processing parameters coupled with qualitycontrol (QC) that makes it possible to assess signalpreservation versus noise attenuation (Coleou et al.,2013). In this work, we propose to revisit some of themain steps in the seismic signal treatment and seismicinversion of broadband seismic data, using the CamposBasin variable-depth streamer seismic data set. The

objective is to propose the best practices to help theinterpreters to reduce the risk in oil exploration whenbroadband seismic data are used. The resulting studyshowcases the benefits that broadband data can bringto reservoir uncertainty management — in this case, atthe exploration stage.

Case study overviewFollowing the high interest in seismic exploration in

the Campos Basin, a variable-depth streamer acquisi-tion with 12 streamers, each 8100 m long, was acquiredby CGG in 2014. The cable depth varied from 10 m at thenear offset to 50 m at the farthest offset, and the sourcedepth was 6 m.

The data processing sequence consisted of swell andlinear noise removal, suppression of seismic interfer-ence, debubbling using the far-field source signaturemodeled from the recorded near-field hydrophone,ghost wavefield elimination (Hu et al., 2014), watercolumn static correction to compensate for water tem-perature variations, 3D surface-related multiple attenu-ation, and regularization onto a 25 × 25 m grid. Aprestack Kirchhoff depth-migration algorithm was usedto perform the imaging after a geologically consistentvelocity model had been built through a multilayer hori-zon-constrained tomography (Masclet et al., 2015b).At the target level, the amplitude spectrum of the finaldata calculated in a 1300 ms vertical window show the−6 dB bandwidth ranging from 4 to 68 Hz — seeFigure 2.

An area of 100 km2 encompassing three explorationwells drilled through postsalt clastic sediments of San-tonian age was chosen for the present work.

A preliminary feasibility study basedon rock-physics analysis was performedon the available well data in the area todetermine if the elastic properties ex-pected from seismic inversion wouldbe able to effectively discriminate be-tween the target lithology classes (basedon a rock-physics petroelastic model).Three wells, A, B, and C, have compres-sional sonic and density logs available inthe reservoir interval, and only well Ahas shear sonic log data. The target in-tervals of this study are the Santonianoil-bearing sandstones. The geophysicalproblem being solved here is the estima-tion of probability of their occurrence inthe test area. Three types of lithofacieshave been defined at the wells: shale,water sandstones, and oil sandstones.Figure 3 shows the acoustic impedancelogs from the three wells, with the high-lighted pay zones clearly showing lowerimpedance values. However, a statisti-cal analysis of the lithofacies types (Fig-ure 4) shows significant overlap amongthe rock types and impedance values

Figure 1. Schematic geological section of the Campos Basin(Marcos Andre Alves, Brazil Round 9, 2007). The interval ofinterest of this study is postsalt turbidities.

Figure 2. (a) Full-stack section through three wells C, A, and B and (b) ampli-tude spectrum calculated in a 1300 ms window centered on reservoir level.

T146 Interpretation / February 2018

change significantly between wells. Wepropose explaining this overlap by thepresence of a depth trend. Withoutremoving this trend, we cannot applycutoff impedance values to directly sep-arate the oil-saturated sand from theother two zones. We therefore “detrend”the acoustic and elastic property valuesby subtracting the compaction trend, de-fined as a linear function of depth, fromthe log property values. This operationallows for more effective separation ofthe pay zone in the detrended acousticimpedance values (Figure 5) which, inturn, allows the acoustic inversion toseparate the oil sands more effectively.Further analysis of the elastic propertiesin well A, where the S-wave velocity andother elastic properties are available,shows that the use of detrended lambda-rho and mu-rho elastic properties (Good-way, 2001) improves the separation ofthe pay zone interval from other facies(Figure 6).

The preceding well-log analysisshowed that an elastic inversion of theseismic data for detrended lambda-rhoand mu-rho will optimally characterizethe oil-bearing sandstones and hence re-duce the uncertainty of the pay zonelocalization. With the seismic field dataas our main input, the broad outline ofour reservoir characterization workflowis as follows:

• seismic data preconditioning• horizon and fault interpretation• spectral analysis and wavelet esti-

mation• optimization of the initial inver-

sion model and parameters• lithofacies definition and classifi-

cation.

Each of the steps will be reviewed indetail in the next sections, focusing onthe specific considerations for broad-band data.

Seismic preconditioning andinversion workflowSeismic data preconditioning

Although the main processing se-quence is usually optimized to best ad-dress imaging-related issues, greaterfocus is placed on delivering final datathat are ready to be input into quantita-tive analysis and reservoir characteriza-tion workflows — such as amplitude

Figure 3. Acoustic impedance log in wells A, B, and C with pay zones high-lighted by low values of water saturation.

Figure 4. Histograms of acoustic impedance values color coded by rock type(shale, water-saturated sandstone, and oil-saturated sandstone) in wells A, B,and C. Impedance values for the same lithology change significantly betweenwells.

Figure 5. Rock-physics plot, wells A, B, and C — combined histogram ofacoustic impedance values colored by rock type from three wells. Left: Beforedetrending, there is significant overlap of the rock types in terms of acousticimpedance values. Right: After detrending, it is possible to isolate someof the oil sand rock type using a detrended acoustic impedance cutoff(−8.5eþ 06 kg∕m3 �m∕s suggested here).

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variation with offset (AVO) studies, direct hydrocarbonindicator (DHI) generation, or acoustic and elastic in-versions. Depending on the final reservoir managementobjectives, additional enhancement steps can be under-taken to optimize the data. It is also worth stressingthat, just as the processes applied are important, appro-priate QCs should be put in place during data acquisi-tion, processing, and preconditioning. Definition of themost relevant attributes, horizons over which they areanalyzed, and statistics to be checked is preferably tail-ored as a function of the project’s final objectives (Ara-man and Paternoster, 2014). By iterating the analysis ofthese attributes, it is possible to quantitatively monitorthe improvement in the seismic data with respect to thefinal aim. Particular attention should be paid to the fol-lowing points:

• wavelet stationarity — vertical and lateral varia-tions in signal frequency, phase, and amplitudes

• levels of coherent and random noise and lateralconsistency in the signal

• prestack signal variability in terms of frequencycontent, phase, and AVO

• calibration of the seismic data with other mea-sured “hard” data such as well logs.

In the case of the present study, the geophysicalproblem being addressed is to best characterize theoil-bearing sandstone by inverting partial angle stacksfor elastic properties. We therefore decided to combinetwo QC approaches:

• Local QC where well log data are available: Seis-mic rock-physics modeling help to assess if thedata are locally calibrated and representative ofthe existing reservoir property knowledge. Thisensures that rock properties can be retrievedfrom seismic amplitudes. We include the follow-ing in those steps: the well-to-seismic tie, AVOcurve comparisons between seismic and syn-thetic, local deterministic wavelet analysis, andmini inversions in the well vicinity.

• Global QC via statistical parameters, calculated atthe survey scale, to capture the global behavior ofthe signal: signal-to-noise ratio (S/N), quality and

anomaly attributes for AVO QC (Coleou et al.,2013), and enhanced bandwidth at every precon-ditioning step. These QCs can be viewed globallyor on selected horizons.

In short: tie the seismic to any other reliable data avail-able, in our case log data, and use statistical QC indica-tors on a more global basis, including horizon-basedQC, for stationarity analysis.

Three preconditioning procedures were performedon the data set, taking special care to preserve low-fre-quency content of the data:

• random noise attenuation applied to gathers• structural filtering applied to angle stacks• spectral shaping applied to angle stacks.

The projective filtering is carried out in the f -xdomain. For this case study, stronger denoising param-eters were chosen for the high frequencies comparedwith the low frequencies. This parameterization con-centrates the energy of the residuals in the high-frequency range, which is more affected by randomnoise, and it better preserves the low frequencies.The filter parameters are optimized using residualanalysis, AVO curve analysis before and after the filterapplication, and AVO attribute analysis to ensure thatnoise is attenuated without affecting the signal.

After gather preconditioning, the decision about an-gle intervals for partial stack calculation is takenthrough data evaluation, AVO analysis tests, and inver-sion tests. The angle intervals should include seismicdata of sufficient quality and subsequently allow recov-ery of the elastic properties in prestack inversion. Theangle stacks were defined as follows: 6°–18° (near),16°–28° (mid), 26°–38° (far), and 36°–48° (ultra-far).

Structurally consistent filtering was applied to anglestacks: It filters along the local dips computed on thereference data. This filter decreases the energy of thelow-frequency component of the data and depends sig-nificantly on the choice of reference data. This filter in-creases the S/N values in all angle stacks, and Figure 7shows the result for the mid angle stack. There is aglobal increase in S/N extracted from the reservoir in-terval visible in the attribute maps (a shift from blue to

red colors) and in the corresponding his-tograms of the S/N values for the reser-voir interval.

After evaluation of the results, differ-ences in the frequency spectrum of thepartial stacks were observed (Figure 8),with the high-frequency part of the spec-trum becoming weaker and the low-fre-quency part of the spectrum becomingmore prominent as the offset increases.This is expected because attenuation ofseismic energy is frequency dependentand increases with the travel path (i.e.,the greater the offset, the greater theattenuation of the high frequencies).

Figure 6. Rock-physics plot, well A — crossplot of lambda-rho versus mu-rhovalues. Top: before detrending; bottom: after the elastic properties have beendetrended. The oil-saturated sand is better separated.

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Spectral shaping was applied to angle gathers to im-prove the homogeneity of the frequency content be-tween angle stacks. An individual matching operatorwas calculated for the mid, far, and ultra-far stacks,using the spectrum of the near stack as a reference.The calculation was done over a short time gate of300 ms to avoid the influence of vertical nonstationarityin the frequency content of the data because no Q com-pensation in amplitude had been previously applied tothe data. A representative set of traceswas selected for the calculation becausea single operator was derived for eachpartial stack and was applied globally.Figure 8 shows the spectra estimationfor angle stacks before and after spectralshaping. A routine step in precondition-ing workflows is a residual misalignmentcorrection between angle stacks. Thiswas tested on the full data set, but noindication of its benefits was found froma seismic inversion point of view (i.e.,no obvious improvement in the well-to-seismic calibration, the global AVO be-havior, or elastic inversion results wasobserved). An example gather is shownin Figure 9f.

Having elastic property logs availableat well A, we were able to perform a lo-cal QC of the calibration of the seismicresponse to known measured data. Weused the sonic, shear, and density logsto calculate the reflectivity at various in-cidence angles using the full Zoeppritzequations (1919). A synthetic prestackdata set was then generated with a 1Dconvolutional model. These syntheticAVO gathers were compared with thereal data at each step in the precondi-tioning sequence, making it possible to check the cali-bration of the AVO response of the seismic data to whatis expected from measured log data. Figure 9 showshow the AVO response at the well A location was im-proved at each step in the preconditioning sequence (in-cluding spectral shaping) as shown by the decreasingdispersion of the points around the regression (whichis preserved).

Coleou et al. (2013) introduce an efficient statisticaltool that helps to quantify and monitor the AVO compli-ance of large amounts of prestack data, as well as iden-tifying areas where further improvement in the dataquality are required. Repeatability attributes calledanomaly and quality, an orthogonalization of crosscor-relation and normalized rms amplitude (Nrms), are cal-culated between real traces and AVO model traces.Focusing the analysis of those attributes over the maininterval of interest (approximately 400 ms centered onthe Santonian interval) at various steps in the precon-ditioning sequence allowed us to statistically evaluatethe improvement in the data. Maps of the quality attrib-

ute calculated for the reservoir interval at the beginningand end of the preconditioning sequence are displayedin Figure 10, illustrating how the similarity of the realand model traces increases during the preconditioningsequence for all the angle stacks across the extent ofthe study area. Figure 11 shows crossplots of the qualityand anomaly attributes calculated in a 400 ms time win-dow at the reservoir level at the beginning and end ofthe preconditioning sequence. After preconditioning,

Figure 7. S/N (in dB) estimated in the reservoir interval before (left) and after(right) structurally consistent filter application for the midangle stack. Maps ofthe S/N are shown in the top panels along with the location of well A. The lowerpanels show the corresponding histograms of the S/N values and indicate thecolor scale for the maps.

Figure 8. Amplitude spectra (in dB) for the angle stacks be-fore (left) and after (right) spectral shaping.

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there is a shift in the value to higher quality and loweranomaly, again indicating a quantitatively better fit be-tween the real and model traces. Using these metrics,we were able to gauge the data quality as well as mon-itor the increase in confidence in the seismic data awayfrom well A where we know it is calibrated during thepreconditioning.

Interpretation of horizons and faultsThe structural interpretation is usually performed

before reservoir characterization and inversion, usingthe seismic amplitude data and seismic attribute analy-sis. It can be further refined after the seismic attributesand elastic properties have been calculated from thedata. In the context of highly reworked geology, withfaulting, potential large displacements, and lateral dis-continuities in the seismic events, the low-frequencyinformation contained in broadband data provides valu-

able information for the interpreter. Previous work, inparticular by Duval (2012), has demonstrated the valueof broadband data for structural interpretation. Wefurther illustrate this with two examples, using ourpreconditioned full-stack seismic cube as input. We firstperformed manual picking of seismic events at the topand bottom of the interval of interest. Second, coher-ency-based attributes, routinely used for fault and frac-ture detection, were calculated. To evaluate the impactof low frequencies on those steps, they were performedon the full-bandwidth broadband data and after applica-tion of a 10 Hz low-cut filter.

During automatic picking, we observe that the seis-mic events on broadband data present a more uniquewaveform, due to the extended bandwidth and reducedside lobes. The resulting reduced uncertainty in pickingcan be seen in Figure 12, which shows the sectionviews of full-bandwidth broadband seismic data

(b) compared with the data withoutlow frequencies (a), after the applica-tion of a 10 Hz low-cut filter. The brightnegative reflection (black horizon) cor-responds to the top, and the positive re-flector corresponds to the bottom (redhorizon) of the zone of interest. Thecharacter (waveform and amplitude)of these reflections is unique on broad-band data and cannot be confused withother events, whereas this uniquenessdisappears after low-cut filtering. Thefiltered data allow for a wider range ofpossibilities of interpretation based onvisual continuity in seismic events withthe same waveform and energy, illus-trating the higher uncertainty in the re-flector definition when the seismic datalack low frequencies.

To further refine the interpretationand reduce its uncertainty, the horizonpicking can be performed on impedanceor relative impedance volumes. It isworth remembering that in the case ofbroadband data, it is possible to havefast-track inversion results for this pur-pose very quickly after the processeddata are available (see section “Optimi-zation of the inversion” of this paper).The inversion process itself presentsthe benefit of slightly extending the databandwidth on the high-frequency side(Pendrel and Van Riel, 2000). It integra-tes low-frequency information from an apriori model with higher frequenciescoming from the seismic data, resultingin a final model closer to the true reflec-tivity model of the earth. Figure 12cshows possible refining of the target bot-tom (purple) interpretation on the rela-tive inversion result (the detailed

Figure 9. AVO response at well A location for (a) synthetic gather, (b) inputgather, (c) after random noise attenuation, (d) after structural filter appliedto angle stacks, (e) after spectral shaping, (f) input gather at the well locationwith Vclay and water saturation curve. Selected event corresponds to the bottomof pay zone. (b-e) The progressive decreasing dispersion of the points aroundthe regression and preserving the AVO response that matches the syntheticresponse (a).

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explanation of this volume calculation is presented inthe inversion chapter of this work).

Another task facing the seismic interpreter is locat-ing subtle features such as faults and fractures withinthe data volume. This task was significantly facilitatedafter the introduction of the coherency attribute byBahorich and Farmer (1995). This attribute gives the in-terpreter a clear visual indication of the continuity be-tween seismic traces. Since that time, other attributesusing the same principle were developed and startedto be widely used for structural interpretation.

Our area of interest is characterized by complex tec-tonics. To accurately define faults, we chose to calcu-late a coherency class attribute called horizon edgestacking. This attribute works well even on volumeswith steeply dipping events, and, with further data pre-conditioning, it is suitable for subsequent automaticfault tracking (more details can be found in Dorn et al.,2012). Figure 13a shows a time slice of this attribute cal-culated from the input broadband seismic stack — theattribute clearly identifies the discontinuities present inseismic events which can be interpreted as faults. Thesame time slice has been extracted from the seismicdata without low frequencies (after low-cut filtering,as shown in Figure 13b). Both clearly highlight discon-tinuities in the eastern part of the map, where thealigned areas of high attribute values (black) are lo-cated in the middle of the area with low-background

values (white). Those discontinuities cutting throughmore continuous reflection zones can be directly inter-preted as faults, and they are also seen in the seismicsection view. In the central part of the time slices,the seismic reflections contain multiple small-scalediscontinuities together with large-scale faults. In theabsence of low frequencies, these two types of discon-tinuities are mixed in the attribute map into an uninter-pretable product (Figure 13b). We interpret the betterdelineation of the faults in the broadband data asbeing due to the information contained in low frequen-cies: The full bandwidth makes it possible to betterhighlight the large-scale faults in zones with small-scale discontinuities. As near-vertical events with lowapparent frequency, their energy is concentrated inthe lower frequencies. This can also be observed fromthe sections in Figure 13: The section view of full-bandwidth data contains distinct faults which appearas low-frequency reflections and do not stand out asclearly after the low frequencies in the data have beenfiltered out.

Wavelet estimationWavelet estimation is a critical step for seismic res-

ervoir characterization workflows because it createsthe bridge between seismic reflections and the reflectiv-ity series. Properly estimated amplitude and phasespectra of the operators that match seismic and log data

Figure 10. Quality attribute (Coleou et al., 2013) maps calculated in a 400 ms window at the reservoir level for each angle stack.(a) Raw angle stacks. (b) Final preconditioned angle stacks showing a shift to higher quality values and, therefore, a better matchwith the AVO model.

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are key to successful acoustic and elastic inversions.We can define two main approaches for wavelet estima-tion — statistical and deterministic. The statisticalmethod uses only seismic data, whereas the determin-istic method uses well log reflectivity (reflection coef-ficients calculated from P-sonic, S-sonic, and densitylogs using the Zoeppritz equations) with seismic data.

We performed the wavelet estimation in the Xeleretetest area in three steps, which can be summarized as(1) statistical zero-phase wavelet estimation, followedby (2) full-phase wavelet estimation at the wells only,and finally (3) a low-frequency phase estimation. Wewill discuss these methods in more detail.

Step 1 — Statistical zero-phase wavelet estimation:The global initial zero-phase wavelet has an amplitudespectrum based on the autocorrelation of the seismictraces. The statistical wavelet for broadband datashould be estimated in a time window that is longenough to cover frequencies as low as possible withthe S/N ratio of the processed data — a 1000 ms win-dow was chosen in this case.

Step 2 — Deterministic wavelet estimation at welllocations, using the crosscorrelation between synthetic

and seismic traces in the target interval and the statis-tical wavelet as additional input information. The stat-istical wavelet estimated in step 1 contains the globalamplitude spectrum of the data in the selected intervaland is used to create synthetic traces using the convolu-tional model (Edgar and van der Baan, 2011). Alteringthe phase and amplitude spectra of the wavelet to reachthe maximum crosscorrelation between the syntheticand seismic traces, we estimate the deterministic wave-let at well locations. This wavelet contains the part ofthe data spectrum which matches the well reflectivityor, in other words, the part of the data spectrum con-taining geologic information. The deterministic waveletextraction allows for a robust estimation of the phaseand amplitude spectra of the operator at well locationswhen log data are available, presenting a reasonablewell-to-seismic tie characterized by the high crosscorre-lation between seismic and well synthetic data, over asufficient time gate. As a general guideline, the extent intime of the well logs should be 2–3 times the waveletlength to obtain a stable estimation of the operator.

Figure 11. Quality and anomaly attributes (Coleou et al.,2013) calculated in a 400 ms window at reservoir level anddisplayed in a crossplot where each point represents a binin the survey. (a) Raw angle stacks. (b) Final preconditionedangle stacks, showing an overall shift to higher values of qual-ity and lower anomaly values, indicating a better fit with theAVO model.

Figure 12. Section view with top and base of the interval ofinterest interpreted: (a) on seismic data without low frequen-cies (low-cut filter 10 Hz applied), (b) on full-bandwidthbroadband seismic data, and (c) on a detrended acousticimpedance section (relative impedance values). It is easierto pick the events on the full-bandwidth data due to thesharper wavelet with reduced side lobes, but the impedancesection provides the clearest demarcation of the interval ofinterest.

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Figure 14 shows the calibrations of wells A and B withthe full seismic stack and the deterministic wavelets ex-tracted from these wells. The estimation at well A wasperformed in a 400 ms time window, and the crosscorre-

lation graph has a well-defined maximum at the value0.91. The wavelet estimation in well B was performedin a 300 ms time window with a crosscorrelation of0.81. The objective of the study is to identify the oil sand,

which can be identified by low values ofSW (water saturation) log in Figure 14.The amplitude and phase spectra of thedeterministic operators are shown in Fig-ure 15. The wavelet estimated in well Bdisplays a lower phase stability, especiallyfor high frequencies, and a lack of low-frequency energy compared with thewavelet estimated in well A. This lateralvariability in the energy concentrated atlow frequencies, which we observe be-tween wells A and B (Figure 15), was con-firmed by an extended lateral analysis:Figure 16a contains the map of seismicenergy corresponding to the signal at5 Hz in a time window of 600 ms aroundthe reservoir calculated for the full stack.We observe that, for these low frequen-cies, the energy of the seismic data ishighly variable, and the difference in sig-nal content observed at the wells is not alocalized phenomenon. When comparedwith other QC done on the full-bandwidthfull stack, these features are observableon other attributes such as the dominantfrequency, bandwidth, or rms of the am-plitudes (Figure 16b–16d), confirming that

the trend of low frequencies can be related to geology.After analysis of the deterministic wavelets at each

well, and to obtain optimal wavelets for acoustic andelastic inversions, the wavelet extraction is performed

Figure 13. Horizon edge attribute time slice and corresponding vertical sec-tions for (a) full-bandwidth broadband seismic stack and (b) 10 Hz low-cut fil-tered seismic stack. The full-bandwidth images provide a sharper image of thefaults and a cleaner discontinuity attribute map.

Figure 14. Well-to-seismic tie panel for two wells: well A andwell B with the corresponding wavelets estimated and cross-correlation functions between synthetic (blue) and seismic(black) traces. The wavelet estimated in well B displays lowerphase stability, especially for high frequencies, and a lack oflow-frequency energy compared with the wavelet estimated inwell A.

Figure 15. Amplitude and phase spectra of wavelets ex-tracted in well A, well B, and multiwell wavelet extractionwhich maximizes the crosscorrelation function calculatedin wells A and B simultaneously.

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using several wells simultaneously. Theprocess is called multiwell wavelet ex-traction and estimates the wavelet thatmaximizes a crosscorrelation functioncalculated in several wells simultane-ously. The “best” wells with reliable cal-ibration are chosen to be used in thiskind of wavelet extraction — wells Aand B in our case. Well C is not useddue to it having the lowest crosscorrela-tion coefficient and will only be used foradditional QC as a blind well.

When a multiwell wavelet is extracted(Figure 15), it repeats the shape of thewavelet “richest” in low frequencies, ex-tracted in well A. For the high frequen-cies, the multiwell wavelet follows thepoorest well wavelet. It is an importantfact to consider in the case where wehave well data in an area with poor low-frequency content. The frequency mapanalysis (Figure 16) performed jointlywith the final wavelet estimation is cru-cial to avoid missing those low frequen-cies in the final wavelet. If this range offrequencies was not represented in theoperator, it would prevent the inversionfrom properly recovering the corre-sponding signal, missing out on one ofthe main benefits of broadband datafor the inversion process. The multiwellwavelet is reliable for mid and highfrequencies, but the phase estimationin low frequencies (the first 10 Hz) re-quires additional analysis, which is ex-plained here as step 3 in our waveletestimation flow for broadband data.

Step 3 — Phase estimation for the lowfrequencies was performed using three al-ternative methods: (1) phase extrapola-tion from mid toward low frequenciesas shown by Schakel and Mesdag (2014),(2) maximum kurtosis determination, and(3) wavelet estimation in wells A and Cthat have the longest interval of logs inthe time domain.

Schakel and Mesdag (2014) suggestthat the phase of the broadband dataat low frequencies follows a linear trendrather than being of a constant value inthis range, and they introduce a practicalapproach to broadband wavelet estima-tion. The phase of the multiwell waveletshown in Figure 15 is extrapolated to-ward low frequencies following this firstphase estimation method, applied in thiscase for frequencies of less than 7 Hz.Indeed, none of the wells shows a reli-able well-to-seismic tie in a time window

Figure 16. (a) Spectral decomposition result. Map of the energy correspondingto the signal of 5 Hz in the full stack. (b) Dominant frequency of the full stack.(c) Root-mean-square energy of the full-stack full bandwidth. (d) Bandwidth ofthe full stack. This frequency attribute analysis was performed jointly with thefinal wavelet estimation and is crucial to avoid missing the low frequencies in thefinal wavelet. The maps indicate that well C is in an area where the seismic haslimited bandwidth.

Figure 17. Phase estimation using the maximum kurtosis method for differentbandwidths. (a) Phase histograms, (b) phase maps, and (c) the spectra for thedifferent frequency bands.

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greater than 400 ms. When trying to in-crease the time gate used for the match,we observe a drop in crosscorrelationthat does not allow us to have confidencein the resulting wavelet.

The maximum kurtosis method isbased on the fact that seismic reflectiv-ity sequences have amplitude distribu-tions that are leptokurtic, that is tosay, they are more heavy-tailed thana Gaussian distribution (White, 1988).This method estimates a constant phaseshift over the input bandwidth. Thismethod is sensitive to the amount ofdata used in the estimation in relationto this threshold. The phase estimationwas performed using four differentbandwidths: 0–10 Hz, 0–20 Hz, 0–30 Hz,and 0–40 Hz in a 2 s time window. Fig-ure 17 shows the maps of the obtainedphase shifts (deviation from zero phase)for each frequency range. The tendencyof phase values to increase toward thelowest frequencies is in accordance withwhat was seen in the multiwell wavelet(Figure 15).

Two of the wells located in the area(wells A and C) have representative in-tervals of sonic and density logs avail-able. However, neither of these wellshave a reliable well-to-full bandwidthseismic tie for the whole interval of logs.Therefore, the wavelets shown in Fig-ures 14 and 15 were estimated in thereservoir interval only. But the completetime interval of available log data wasused in these wells to run a comparativeestimate of the phase for the lowfrequencies between 0 and 10 Hz. Fig-ure 18 shows the calibration of wellsA and C to the low-frequency part ofthe seismic: 0–10 Hz. Well A was cali-brated with low-pass-filtered seismicin a 0.9 s long interval with crosscorre-lation of 0.85, and well C was calibratedin a 0.8 s long interval with a crosscor-relation of 0.93.

The results of the three independentmethods of low-frequency phase evalu-ation are compiled in Figure 19: the stat-istical maximum kurtosis method, thedeterministic method through calibra-tion of wells to the low-frequency partof the seismic, and an empirical ap-proach proposed by Schakel and Mes-dag (2014) to follow the linear trendextrapolating phases from mid to lowfrequencies. All methods delivered con-sistent results, and the multiwell wave-

Figure 18. Calibration of seismic to well log synthetics at wells A and C usingthe low-frequency part of the seismic (0–10 Hz) helps decrease the uncertainty inphase estimation for low frequencies.

Figure 19. Phase estimated for low frequencies using three different methods.All methods delivered consistent results, and the uncertainty of the waveletphase estimation at low frequencies can be inferred from the spread observedbetween the different methods and the multiwell wavelet.

Figure 20. Calibration of angle stacks with well A well log synthetics and cor-responding wavelets extracted for use in the elastic inversion. AI, acousticimpedance curve; SW, water saturation curve in well A.

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let with the phase shown as a black line in Figure 19 wasused in the following inversion. The uncertainty of thewavelet phase estimation at low frequencies can be in-ferred from the spread observed between the differentmethods tested here: approximately ±15° around themultiwell wavelet at 5 Hz.

To extract wavelets for the elastic inversion, thesame methodology was used to extract angle-depen-dent wavelets for near, mid, far, and ultra-far anglestack — the result is illustrated in Figure 20. Note that

in the elastic case, only well A had adequate logs to per-form the deterministic wavelet extraction, and the otherwells do not have measured VS logs.

Optimization of the inversionIn accordance with the rock-physics analysis pre-

sented in the case study overview, two deterministicinversions were performed sequentially with the corre-sponding facies classification: poststack- and prestack-

constrained sparse-spike inversions.A poststack or acoustic-constrained

sparse-spike inversion creates an acous-tic impedance model from the seismicreflection data. The P-impedance isestimated by minimizing an objectivefunction that contains multiple terms:residuals (the mismatch between seis-mic and synthetic), a contrast misfit(controls sparseness), a trend misfit(the difference between the final resultand the initial impedance model), and asoft spatial misfit (controls the lateralsmoothness of the result).

Deterministic inversion requires alow-frequency model to bring into theinversion result the low-frequency infor-mation that is missing in seismic data.The frequency bandwidth of this initialmodel depends on the lowest usable fre-quency (in terms of the S/N) of the seis-mic data spectra. A wide gap of missinglow frequencies (e.g., 0–10 Hz typicallyfor conventional seismic data) will re-quire building a detailed initial model.The narrower the gap of absent seismicfrequencies, the more data driven theinversion is. Initial models for conven-tional seismic data are usually obtainedfrom low-pass-filtered elastic attributeslogs at the well locations, followed byan interpolation method (kriging for in-stance). This method of model buildingrequires stratigraphic model (frame-work) to perform interpolation insideit. This stage of the inversion workflowbrings significant uncertainty to the in-version as the low-frequency modelcreated through log interpolation is con-ditioned by the number of wells in thearea, the reliability of the stratigraphicmodel used in the interpolation andextrapolation of the sparse impedances,and lateral variability in elastic proper-ties (Pendrel and Van Riel, 2000).

Seismic data acquired using variable-depth streamers are ideally suited for in-version because they provide more ofthese missing low frequencies (typicallydown to 3 Hz), hence decreasing the

Figure 21. Crossline 6280 — time-frequency display of the energy of (a) initialacoustic impedance initial model derived from seismic velocities and (b) full-bandwidth seismic stack. Significant overlap around an average frequency of3.5 Hz can be observed, confirming that no additional information is requiredto recover a complete frequency bandwidth during the inversion.

Figure 22. Random line through three wells. (a) Seismic stack, (b) initial model,and (c) acoustic inversion result. Well lithology logs are superimposed on theinversion results. The oil sand lithology can be seen to match zones of low acous-tic impedance as expected.

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dependence on well data to build the inversion low-fre-quency initial models, which would now only need tohave a bandwidth of 0–4 Hz). The initial model createddirectly from seismic velocity without use of a strati-graphic model and seismic logs was used in our case.Because the trace-based inversion algorithm was used(not stratigraphic inversion), the structural frameworkwas not necessary to perform inversion. Figure 21 illus-trates the time-frequency information contained in1) the acoustic impedance initial model calculatedfrom seismic velocities and density cal-culated from a sonic-density relation-ship derived at the well and 2) theseismic full stack. Significant overlaparound an average frequency of 3.5 Hzcan be observed, confirming that no ad-ditional information is required to re-cover a complete frequency bandwidthduring the inversion.

Figure 22a–22c shows an acoustic in-version plot including seismic stack, ini-tial model, and impedance obtainedfrom the acoustic inversion result. Fig-ure 23 shows the QC of acoustic inver-sion results at the wells, comparing theimpedance values of well logs and in-verted values along wells. Because nowell interpolation was used in the low-frequency model construction, the re-sults are the direct consequence of thecalibration of the seismic velocity modeland seismic amplitudes. This plot dem-onstrates the good match between thefinal inversion results and the measured data fromwells, confirming the possibility to further use the inver-sion results for qualitative and quantitative interpreta-tion. Considering the low level of vertical detailsintroduced by the initial model (Figure 22b), the goodmatch observed is due to the information derived fromthe broadband seismic data (and the low frequencies inparticular). We calculated the frequency-domain nor-malized root-mean-square (fNrms) difference betweenreal data and inversion synthetic data. This attribute,displayed in Figure 24 for crossline 6280 that runs closeto wells A and C, is useful to assess the quality of thematch between inverted synthetic and real data as afunction of frequency. We can observe in our case thatinformation is contained in the broadband data over alarge range of frequencies from 3.5 to 55 Hz. The matchthen degrades toward frequencies of 70 Hz where novaluable information is retrieved by the inversionprocess.

Prestack or elastic-constrained sparse-spike inver-sion creates a set of elastic models by simultaneouslyinverting multiple seismic partial (angle or offset)stacks. In our case, and following the results fromthe initial rock-physics analysis, the acoustic (IP) andshear (IS) impedance volumes obtained from the inver-

sion were used to calculate the Lamé impedanceslambda-rho and mu-rho.

Figure 25 shows a section view of these parametersafter applying detrending, and Figure 26 presents theQC at well A comparing measured values from the welllogs to the inverted values.

A good match at the wells was observed for theacoustic and elastic inversion results from the broad-band data (Figures 23 and 26). The initial model forinversion (Figure 22b) was created directly from the

Figure 23. Well QC of acoustic inversion. Black, acoustic impedance valuesfrom well logs; (a) blue, acoustic impedance initial model; and (b) red, invertedacoustic impedance. This demonstrates the good match between the final inver-sion results and the measured data from wells, confirming the possibility to fur-ther use the inversion results for qualitative and quantitative interpretation.

Figure 24. The fNrms (the difference between inverted realand synthetic data for crossline 6280). We can observe a goodmatch from 3.5 to 55 Hz. The match then degrades towardfrequencies of 70 Hz where no valuable information is re-trieved by the inversion process.

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seismic velocity without any use of filtered well dataand interpolation models. Thus, no horizon interpreta-tions were necessary to create the initial model. This

makes broadband data inversion more data driven thanthe conventional seismic data inversion, where theintervention of geophysicists has a much bigger impact

on the result through the building of amore detailed initial model. It alsospeeds up the inversion workflow, mak-ing it possible to achieve fast-track in-version results very quickly after thefinal processed seismic data becomeavailable and therefore providing betterinformation earlier in the workflow tosupport drilling decisions.

Lithofacies definitionand classification

With the results obtained from theacoustic and elastic inversions, deter-ministic and probabilistic interpreta-tions were performed to localize thereservoir pay zone.

Deterministic interpretation was per-formed by application of a cutoff on theinverted properties, as defined in Fig-ure 5 for acoustic impedance values.This result is shown in Figure 27a. Thiszone matches with the pay zone in thewells (orange), but the main limitationof this approach is the absence of infor-mation regarding the uncertainty relatedto the classification. The ranges of faciesvalues we aim to identify (via acoustic

impedance or lambda-rho and mu-rho in our case) showsignificant overlap with the other facies defined at thewells. No related information is taken into accountwhen using a simple threshold for defining limitsbetween lithoclasses, whereas the uncertainty of theclassification will directly result from the degree ofoverlap.

A more robust probabilistic approach was under-taken through the application of a supervised Bayesianclassification technique to infer the probability of pre-defined lithofacies from the inversion results (Mukerjiet al., 2001, Pendrel et al., 2006). To characterize the payzone, the three lithofacies (shale, water sands, and oilsands) were first defined from well log informationsuch as porosity and saturations. The method then re-quires the creation of probability density functions foreach facies, which determines the probability that a par-ticular combination of elastic parameter values repre-sents a given facies. This was done by creating ahistogram for acoustic inversion or a crossplot of theelastic parameter curves (color coded by the faciesin Figure 28) and fitting a probability density function(PDF) to the samples associated with each of the facies.A prior probability of each facies can be estimated, in-dicating the relative proportion of each facies that isexpected within the region of interest, or, as it was donein our case — assuming that the a priori probabilitiesequal: 33.3% — shale, 33.3% — oil sand, and 33.3% —

Figure 26. QC of elastic inversion in well A. Blue curve,lambda-rho calculated from well logs in well A; red curve,mu-rho calculated in well A; and purple curves, the corre-sponding estimates from the inversion, which indicate a goodmatch with the well data.

Figure 25. Elastic inversion result after detrending. (a) Lambda-rho and (b) mu-rho. Detrending of the elastic properties from inversion provides a better sep-aration of the lithoclasses for interpretation.

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water sand. These facies-conditionedPDFs and a priori geological informa-tion are then combined within a Baye-sian inference framework to generatefacies probability volumes from the in-verted elastic parameter volumes. PDFswere built using log information re-sampled to 4 ms sample rate in time do-main, same as seismic sample rate.Figure 28a shows the PDFs establishedusing detrended impedance logs in wellsA, B, and C. These PDFs were applied toour acoustic inversion result and givethe pay probability volume shown inFigure 27b. Figure 28b shows the PDFsestablished for the detrended lambda-rho–mu-rho pair of parameters, and Fig-ure 27c represents the result of faciesclassification using the elastic inversionresult.

Comparing the classifications of theelastic (Figure 27c) and acoustic (Fig-ure 27b) inversions, a large reductionin the predicted oil sands zone is ob-served. The oil sands probabilities atthe wells in the elastic and acousticcases are shown in Figure 29 and arecompared with the water saturationlogs. Although the prediction of thepay zone at the wells still matches withlog data, much of the predicted oil sand-stones obtained in the acoustic case isno longer present in the classificationfrom the elastic case. The classification performed us-ing acoustic inversion results tends to overestimate theamount of oil sands, whereas switching to the elasticdomain results in a more conservative prediction ofthe pay zone in the area.

The benefits of broadband data for inversion and fa-cies classification have been previously illustrated, inparticular, by demonstrating the uplift brought by thelow frequencies in recovering the absolute values ofelastic properties and subsequent reservoir delineation,in particular in the case of thick reservoirs (Lafet et al.,2012). In the inversion process, using broadband dataresults in more seismic-driven inverted property vol-umes. The final results are less dependent on the initialmodel building, which can often be a major source ofuncertainty. Another aspect worth mentioning is thefact that, within the seismic bandwidth, a better sepa-ration of the facies is achieved when low frequenciesare present. We illustrate this aspect by showing thetraining sets obtained by crossplotting lambda-rhoand mu-rho from wells applying 2 and 10 Hz low-cutfilters (Figure 30). The amount of overlap between fa-cies (and therefore of uncertainty in the classification)is significantly increased when the lower frequenciesare filtered out.

Figure 27. Interpretation of inversion results with well lithology logs superim-posed: (a) Detrended acoustic impedance with cutoff threshold applied to sep-arate the oil-saturated sandstone. (b) Pay probability obtained through Bayesianclassification of detrended acoustic inversion result (c) Pay probability obtainedthrough Bayesian classification of detrended elastic inversion result. The elasticinversion pay probability provides a much more conservative interpretation ofoil sand distribution.

Figure 28. The PDFs used to perform the Bayesian lithoclas-sification. (a) Detrended acoustic inversion PDFs and (b) de-trended elastic inversion PDFs.

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ConclusionFollowing the acquisition of a variable-depth

streamer survey by CGG in 2014 and the processingof the seismic data, a specifically adapted broadbandseismic reservoir characterization workflow (includingdata preconditioning, structural interpretation, waveletestimation, seismic inversion, and facies classification)was performed to characterize the extent of the oil-bearing Santonian sandstones identified at three explo-ration wells. These steps should contribute to a betterunderstanding of the geology in the area, ultimatelyleading to the building of a reliable reservoir modelpopulated with realistic properties.

We showed a seismic data imaging flow that usessparse log data for local QC around the wells andglobal, horizon-based QC attributes elsewhere. We illus-trated the potential difficulties and particular care thatshould be taken when dealing with broadband data, andwe showed in which aspects it can provide additionalvalue to the interpreter compared with conventionaldata. Lithofacies classification cubes of the target payzone have been computed from this workflow, showinga good match with observations at the wells and alongwith a quantification of the uncertainty inherent in theclassification method.

Several aspects of this work suggest that the benefitsof broadband data observed when running deterministic

inversion should extend to more ad-vanced inversion processes. In particu-lar, we showed how broadband dataalso benefit the structural interpretation(geology), geometric attributes, and PDFconstruction aspects, all of which canbe integrated into a stochastic inversionworkflow. A natural extension to thiswork would therefore be to further ex-plore how broadband data can be usedas an input to geostatistical workflows.

AcknowledgmentsThe authors would like to thank CGG

Multi-Client & New Ventures for sharingtheir data and good dialog during theproject, as well as for their permissionto publish this work. We would also liketo thank R. Taylor, H. Hoeber, and R.Porjesz for their feedback and construc-tive discussions.

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