mCSEM data interpretation for hydrocarbon exploration:A fast interpretation workflow for drilling decision

Marco Polo P. Buonora1, Jorlivan L. Correa2, Luciano S. Martins2, Paulo T. L. Menezes3,Emanuel J. C. Pinho2, Joao L. Silva Crepaldi2, Mirela P. P. Ribas2,Sergio M. Ferreira2, and Rafael C. Freitas4

Abstract

In a hydrocarbon exploration workflow, marine controlled-source electromagnetic (mCSEM) data are usu-ally acquired after seismic interpretation for prospect identification and close-to-the-drilling decision making.Therefore, the mCSEM interpreter must provide quick answers to the asset teams in a way that the EM inter-pretation can add value to that decision. To achieve that goal, Petrobras developed a fast-track mCSEM inter-pretation workflow that consists in identifying anomalies in the mCSEM data set by frequency normalization,and then performing 1D CMP inversions followed by 2.5D polygonal inversions. The proposed workflow wassuccessfully applied to several mCSEM surveys offshore Brazil. We evaluated an application in a complex geo-logic setting where the reservoir dips toward allochthonous salt. The reservoir appears as a flat spot in theseismic section, but with no significant amplitude variation with offset response. The mCSEM analysisconfirmed the seismic anomaly and extended it northward. Two drilled wells corroborated the mCSEMinterpretation.

IntroductionSeismic reflection is the real workhorse of the oil in-

dustry due to its high resolution. Seismic applicationsextends from regional exploration with 2D surveys(Matias et al., 2011), going through prospect identifica-tion and appraisal with 3D data (Foster et al., 2010), andreservoir monitoring with 4D surveys (Lumley, 2001).On the other hand, electromagnetic (EM) methods usu-ally play a minor role in the oil industry because theycannot achieve a subsurface image with comparablespatial resolution as provided by seismic methods.Classical applications of EM methods for hydrocarbonexploration include time-domain electromagnetic re-connaissance surveys for detecting hydrocarbon alter-ation plumes (e.g., Smith and Rowe, 1997; Menezes andMorais, 2003), andmagnetotelluric (MT) surveys to helpinterpretation in areas with poor seismic imaging, as inrough topography regions (Zerilli et al., 2012), subcar-bonates (Travassos and Menezes, 1999), subbasalt(Menezes and Travassos, 2005, 2010), and subsalt (Hov-ersten et al., 2000) exploration.

The rise of the marine controlled-source electromag-netic (mCSEM) in the very beginning of the 2000s (Ei-

desmo et al., 2002; Ellingsrud et al., 2002) represented alandmark in reservoir evaluation. Since then, the EMmarket has experienced fast and continuous growthin the oil industry (Constable and Srnka, 2007; Consta-ble, 2010; Strack, 2014).

Petrobras had its first mCSEM experience in 2004/2005 (Buonora et al., 2005), by the time the method be-came available at commercial scale (Constable, 2010).Three multiclient experimental surveys were acquiredover known oil fields in the southeast Braziliancontinental margin. These surveys led to a correct inter-pretation of the known reservoirs and the identificationof new ones. After that, Petrobras took the decision ofbuilding an internal group dedicated to EMmethods. Tothat end, Petrobras hired some EM experts, mixed themwith new talented young geophysicists, and then pro-moted an intensive investment in continuous educationof all its personnel. Following this guideline, Petrobrassupports such academic EM consortia as Scripps (Uni-versity of California) and the Consortium for Electro-magnetic Modeling and Inversion (University ofUtah) in USA and the Universidade Federal do Par(UFPA) and Observatrio Nacional in Brazil. Beside,

1Petrobras, E&P-EXP/GEOF/MNS, Rio de Janeiro, Brazil and Instituto de Geociencias/UFF, Niteroi, Brazil. E-mail: mpolo@petrobras.com.br.2Petrobras, E&P-EXP/GEOF/MNS, Rio de Janeiro, Brazil. E-mail: jorlivan@petrobras.com.br; luciano_martins@petrobras.com.br; emanueljcp@

petrobras.com.br; joaolucas@petrobras.com.br; mirela.ribas@petrobras.com.br; sergio_ferreira@petrobras.com.br.3Petrobras, E&P-EXP/GEOF/MNS, Rio de Janeiro, Brazil and DGAP/FGEL/UERJ, Rio de Janeiro, Brazil. E-mail: ptarsomenezes@pq.cnpq.br.4Petrobras, E&P-EXP/IABCS/PN, Rio de Janeiro, Brazil. E-mail: rcdfreitas@petrobras.com.br.Manuscript received by the Editor 3 October 2013; revised manuscript received 3 December 2013; published online 20 May 2014. This paper

appears in Interpretation, Vol. 2, No. 3 (August 2014); p. SH1SH11, 11 FIGS.http://dx.doi.org/10.1190/INT-2013-0154.1. 2014 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.

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Special section: Interpretation and integration of CSEM data

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the company created in-house EM courses aiming totrain geophysicists and promote the EM culture tothe asset teams.

In the 20072009 triennium, Petrobras established atechnical cooperation agreement with the Schlum-berger Brazilian Research Geoscience Center, held inRio de Janeiro, Brazil. The main scope of this co-operation project was to develop an EM interpretationworkflow and integrate the deep-reading EM technolo-gies into the full cycle of oil field exploration and devel-opment. This project was responsible, among otherthings, for the first marine MT (de Lugao et al., 2008;Gallardo et al., 2012) and full-azimuth mCSEM (Zerilliet al., 2010) surveys acquired offshore Brazil.

In 2011, Petrobras embraced an EMGS proposal toacquire a large multiclient mCSEM campaign acrossseveral Brazilian offshore basins (Figure 1). Data ac-quisition started in the Barreirinhas Basin (equatorialmargin), followed by the Ceara, Potiguar, Sergipe-

Alagoas, Espirito Santo, and Campos Basins. In total,mCSEM data summed more than 5000 km2 coverage(Lorenz et al., 2013). The receiver spacing varied from500 m for detailed prospect characterization to 2000 mfor regional surveys in frontier exploration areas. Fea-sibility studies were responsible not only for thereceiver positioning, but also for other relevant surveyparameters, such as sampling frequencies and towingdirections.

Interpretation of that huge amount of data, at somany different basins and prospects with different timeschedules to provide quick answers to allow the assetteams to take drilling decisions, is a big challenge. Tothat end, Petrobras developed a fast-track explorationworkflow consisting of three main steps: (1) frequencyratio normalization, (2) CMP inversion, and (3) 2.5Dpolygonal inversion. Seismic lines and induction welllogs, when available, provide important constraints tothe proposed workflow.

In the present paper, we show a casehistory in a complex geologic area in Es-pirito Santo Basin. We acquired and in-terpreted, following the herein proposedworkflow, a small 3D mCSEM surveyover a lead mapped in between saltdomes. The mCSEM survey confirmedthe seismic anomaly and guided the de-cision to drill the prospect leading to amajor discovery in the area. Two suc-cessful wells intercepted hydrocarbonlayers at 3720 m depth. For reservoirappraisal phases, we are applying moredetailed mCSEM interpretation work-flows including 3D modeling/inversionschemes together with well results.

Geologic settingThe Espirito Santo Basin (ES in Fig-

ure 1) is a typical passive margin basin,with the tectonic evolution starting bythe time of the breakup of Gondwana.Cainelli and Mohriak (1999) define fourregional sedimentary megasequencesalong the Brazilian continental margin:prerift, continental, transitional, andmarine. These sequences, usually sepa-rated by erosional unconformities, arerelated to the prerift, rift, and passivemargin evolutional phases of the SouthAtlantic Ocean opening tectonic event(Asmus and Ponte, 1973).

The prerift megasequence occursonly in the northeastern margin, on-shore and offshore (Cainelli and Moh-riak, 1999). Thick siliciclastic depositsoccur between the Espirito Santo andSergipe-Alagoas Basins, and tholeiiticbasalts occur in the Campos and SantosBasins. The transitional megasequence

Figure 1. Overview of survey locations of the Brazilian mCSEM 2011/2012 cam-paign offshore Brazil. Sedimentary basins: BAR, Barreirinhas; CE, Cear; POT,Potiguar; SEAL, Sergipe-Alagoas; ES, Espirito Santo; CA, Campos; and SAN, San-tos. Modified from Lorenz et al. (2013).

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marks the transition between rift and drift tectonicphases. Aptian evaporites, mainly halite and anhydrite(Asmus and Ponte, 1973), form thick sedimentary sec-tions in Santos, Campos, and Espirito Santo Basins. Salttectonics affected the overlying rocks and created aseries of listric faults in the evacuation zone, thrustfaults, and intraslope minibasins between salt domesor salt walls (Cobbold et al., 1996). Thermal subsidenceassociated to lithospheric cooling led to the depositionof the marine megasequence. This sequence comprisesa major transgressive-regressive cycle between theLower Cretaceous and Recent (Milani et al., 2000). Shal-low-water carbonates overlaid by thick siliciclasticpackages (platform shallow coastal fans and turbiditedeposits) are the main lithologies of the marine mega-sequence.

In Espirito Santo Basin deep-water regions, the ob-served halokinetic structures correlate with sedimen-tary depocenters. The mobilization of salt in theAlbo-Cenomanian period resulted in turtle back struc-tures and extensional structures such as autochthonoussalt (pillows and diapirs) in the Cretaceous. Allochtho-nous salt (apophyses, salt tongs) occurs from the UpperCretaceous to the Lower Tertiary. The salt tectonicsalso affect the sea bottom morphology at the distal por-tion of the basin (Mohriak et al., 2012). Figure 2 illus-trates the complex geologic setting of the deep-waterEspirito Santo Basin. Huge diapirs and allochthonoussalt layers form a large tongue positioned in betweensiliciclastic younger sediments (Upper Cretaceous toTertiary).

The main exploratory plays in the deep-water Espir-ito Santo Basin are reservoirs dipping toward the saltflanks. New subsalt plays, in analogy to the Gulf ofMexico (Hart and Albertin, 2001; Wilson et al., 2002)and Pricaspian (Volozh et al., 2003) basins, are underinvestigation.

The prospect herein investigated stands in betweenallochthonous salt dipping toward the salt flank.Autochthonous salt occurs around 3000 m down belowthe prospect. The mapped lead appears as a flat spot inthe 3D seismic, though with no significant amplitudevariation with offset (AVO) anomaly. That uncertaintyin the seismic response led to the acquisition of mCSEMdata to test the presence of hydrocarbon-bearing rocks.

mCSEM data setThe 3D mCSEM acquisition program covered a

21 km2 area in the ultradeep-water portion (averagewater depth of 2000 m) of the Espirito Santo Basin.EMGS acquired 38 mCSEM receivers deployed equallyspaced 1 km apart in a rectangle geometry (Figure 3).Presurvey feasibility studies defined a full azimuthsurvey with northwestsoutheast/northeastsouthwesttowing directions (Figure 3) and the source waveformto optimize sensitivity of the resulting data to the inves-tigated lead. The chosen complex waveform (Figure 4a)has the advantage of equally distributing energy alongfour main frequencies between 0.151.4062 Hz(Figure 4b).

We applied an advanced workflow (Zerilli et al.,2010) to process data at each receiver location by

Figure 2. Seismic section in the deep-water Espirito Santo Basin showing geometry of allochthonous and autochthonoussalt bodies.

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correcting, instantaneous measurements of dipolelength, dipole moment, dipole altitude, feather angle,and dip. The data processing resulted in high-qualityamplitude and phase data to a source-receiver offsetof 12 km. For the highest frequencies, maximum rangesof 45 km are typical (Figure 5).

Frequency ratio normalizationA typical preliminary step in mCSEM interpretation

is the use of data normalization to generate anomalymaps. In this procedure, data from each receiver, ata chosen fixed offset, are divided by data representingthe background resistivity (i.e., regional geology sur-rounding the reservoir). We then grid the normalizedvalues at all receivers to determine the areal distribu-tion of the resistivity anomalies. Of course, the correctchoice of the background response is a critical issue.

Herein, we define the normalized value N asErxEref 1, where Erx is the inline electric field atthe receiver and Eref is the reference inline electric fieldrepresenting the background.

Two traditional methodologies include the normali-zation by a reference receiver and normalization by atheoretical response of a 3D background resistivitymodel. Each one has advantages and drawbacks. Theuse of a reference receiver relies on the assumption thatthe chosen receiver describes the geoelectrical back-ground. This can often be supported by seismic imagesavailable along the towlines. A drawback is the bathym-etry effect (Li and Constable, 2007) that cannot be re-moved or attenuated in the normalized data. Thenormalization by a 3D background synthetic responsehas the advantage of mitigating the bathymetry effectand highlighting anomalies due to resistive bodieswithin the sedimentary section. The major problemof that approach in the exploration phase is the needto have comprehensive knowledge about the resistivity

distribution in the study area. Although 3D seismic isroutinely available to provide the necessary structuraland stratigraphic constraints to the model building, theabsence or scarcity of drilled wells contributes to apoor resistivity information.

Frequency ratio (FR) normalization (Buonora et al.,2006) consists of computing the ratio between high- andlow-frequency mCSEM amplitude data at a given fixedoffset. This ratio is calculated for each receiver in a sur-vey. The method assumes that different frequencies willrespond differently to subsurface properties due to theskin depth effect. Lower frequencies will tend to imagethe regional background geology, whereas higherfrequencies will be sensitive to localized bodies. Both

Figure 3. Acquisition geometry for the CSEM survey in theEspirito Santo Basin. Also shown are the lead contour andwells locations. The towlines are cropped in the figure, butthey do extend 10.5 km away from each end receiver.

Figure 4. Waveform for the CSEM survey, shown in the(a) time and (b) frequency domains. Courtesy of EMGS.

Figure 5. Typical data set for the Espirito Santo survey,showing the overall good quality of the inline electric field.

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cases will have, to some degree, the influence ofbathymetry. Hence, by using FR normalization, onecan expect to mitigate the bathymetric effect in slopeareas and enhance the target anomalous response.

To illustrate FR performance, we considered an east-bound downward slope on the seafloor with a 2000 munevenness along a 30 km profile (Figure 6a). The 2Dgeoelectric model comprises the seawater (0.3 ohm-m)

and a 1 ohm-m homogeneous background with an em-bedded thin (50 m) resistor of 20 ohm-m. We calculatedthe 2.5D mCSEM response (Abubakar et al., 2006) at 11receivers equally spaced at 1 km. Figure 6 shows differ-ent normalization plots at the same fixed offset of5500 m. Figure 6b displays the data normalization bythe intowing radial response at 1.25 Hz of the Rx1receiver, Figure 6c displays the normalization by theout-towing response at 1.25 Hz of the Rx11 receiver,and Figure 6d shows the FR normalization calculatedat the 1.25 Hz0.25 Hz ratio. The bathymetric effectcan be clearly observed in the normalization by refer-ence sites with the presence of spurious anomalies.In Figure 6b, negative anomalies appear at both sidesof the depreciated positive anomaly associated withthe embedded resistor. In Figure 6c, a positive falseanomaly appears in the shallower portion of the profile.On the other hand, the FR attenuates the bathy-metric effect highlighting only the anomaly of interest(Figure 6d).

We present in Figure 7 the FR normalization map ofEspirito Santo data at 2.0 Hz0.16 Hz ratio with a fixedoffset of 3250 m. The prospect boundary is displayed tohelp in the interpretation. As expected, allochthonoussalt bodies can be associated with the highest anomalyvalues (greater than 1.30 in Figure 7). The prospectalso shows expressive anomalies in the 0.7 to 1.3 range,indicating the presence of a resistive body in depth.Those anomalies extend the previously mapped seismicanomaly in the northern and southeastern portions.

CMP inversionThe 2.5D and 3D mCSEM inversions are routinely

used to produce quantitative estimation of the resistiv-ity distribution in the subsurface. Available forwardsolvers use three discretization methodologies, i.e.,

Figure 6. Different types of data normalization for a syn-thetic mCSEM data generated by the geoelectric model shownin (a). (b) Normalization by the intowing of Rx1 (1.25 Hz).(c) Normalization by the out-towing of Rx1 (1.25 Hz).(d) FR normalization (1.25 Hz0.25 Hz).

Figure 7. FR normalization map (2.0 Hz0.16 Hz, 3.25 kmoffset) for the Espirito Santo survey. The two successful wells(W1 andW2) are located within the anomalous values. Line L1is the line used for the 1D and 2.5D inversions.

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finite differences, finite elements, and integral equation.In spite of providing a comprehensive picture of thesubsurface, the computational cost is high and too slowfor early stage exploration purposes.

To yield a fast interpretation workflow, Petrobras de-veloped a laterally constrained 1D CMP inversion usinganalytical derivatives (Silva Crepaldi et al., 2011). Thegoals of the CMP interpretation are twofold: quicklyidentify resistivity anomalies probably associated tothe mapped leads and estimate a resistive backgroundmodel to serve as a starting model for the 2.5D polygo-nal inversion.

The proposed method minimizes the least-squaresresidual between data in the CMP domain and the re-sponse of a set of 1D isotropic layers at each CMPgather. These layers are laterally connected by a mini-mum horizontal gradient constraint. That constraintimproves the inversion stability, and it recovers geo-electric sections with geologic meaning, even for 3Dcomplex geologic settings (Silva Crepaldi et al.,2011). Furthermore, by building the Jacobian matrixwith the analytical derivatives of the 1D EM responseswith respect to resistivities, we improve the perfor-mance of the code one order of magnitude faster thanconventional numerical methods (Silva Crepaldiet al., 2011).

The CMP code was implemented as a C built-in plug-in of an in-house seismic interpretation platform. Such

integration allows the user to combine seismic, well log,and geologic information in an easy way to build thestarting model.

Although we have applied the CMP inversion to theinline electric field of all towlines, we examine herein,the results of a single southwestnortheast line (L1 inFigure 7). That line crosses the wildcat well and enablesthe comparison between the mCSEM interpretation andthe well results.

Figure 8a shows the starting model in the inversiondomain with an extended lateral length of 1500 m be-yond the receiver coverage and 6000 m at depth. Wefixed resistivity (100 ohm-m) and boundaries of the saltbodies following the seismic interpretation. Outside thesalt, the model cells are populated with a free 1 ohm-mresistivity and uniform weights for vertical and horizon-tal smoothness, except for the fixed water layer. We ranthe inversion for two high frequencies, 1.4062 and2.0312 HZ, because the primary goal was to test resis-tivity anomalies associated to the prospect. The inver-sion procedure took less than 20 min in a Linux HP 820workstation with 128 GB RAM. That allows the inter-preter to quickly perform several inversion runs withdifferent model parameterizations. This is importantto check the stability and the ambiguity of the solutions.

The final model shows a smooth resistivity anomalyaround the seismic anomaly (Figure 8b) at 3800 mdepth. W1 well intercepted a 100-m-thick hydrocarbon

Figure 8. (a) Line L1 Starting model andmesh for the 1D CMP inversion corenderedwith the seismic amplitude along line L1.The yellow rectangle indicates the positionof the seismic flat spot. (b) Line L1 Theresult of that inversion corendered with theinduction log of W1 well and the seismic am-plitude. Note the strong resistivity anomaly as-sociated with the prospect. This anomalyextrapolates northeastward the seismic re-sponse as shown in Figure 8a.

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layer, at 3720 m depth, with an average resistivity of70 ohm-m (Figure 9). The smooth resistivity anomalyin Figure 8b shows a good depth correlation with theinduction log of W1, and in addition, it extends the re-sistivity anomaly associated with the prospect north-ward, beyond the previously known seismic anomaly.

2.5D polygonal inversionThe 2.5D nonlinear inversion algorithms are rou-

tinely used in the interpretation of mCSEM data duetheir accuracy in high-contrast regions. Most existingmethods discretize the domain of interest into subdo-main cells with unknown resistivity parameters to bedetermined and then apply an optimization strategyto fit the observed and calculated data responses. Sev-eral algorithms are available in the literature (e.g.,Commer and Newman, 2008; Plessix and Mulder, 2008;Abubakar et al., 2009) to predict resistivity distributionin the model cells.

The polygonal anisotropic inversion (Zerilli et al.,2011) adopts a different approach in which the principalidea is to use a structure-based algorithm to reconstructresistivities, and shapes of regions of interest based on apriori independent information given by seismic inter-pretation and well logs, if available. The algorithm isbased on a Gauss-Newton minimization with multiplica-tive regularization and a line-search scheme to stabilizethe process (Habashy and Abubakar, 2004). The shapesof the interesting 2D regions are defined by their verti-ces (nodes) and can be reconstructed along with theirpositions and resistivities. The forward algorithm uses afrequency-domain staggered-grid finite-difference solu-tion to the total-electric-field Helmholtz equation (Abu-bakar et al., 2006). The inversion employs an adjointroutine to compute the Jacobian matrix, speeding upthe inversion run time (Zerilli et al., 2011).

In the present paper, we use the CMP inversion re-sults to determine the background resistivity to con-strain the polygonal inversion. To that end, we ranthe CMP code with the same starting model shown inFigure 8a, but with a different frequency range. We in-verted the mCSEM data for all five frequencies (0.1562,0.4688, 0.7812, 1.406, and 2.031 Hz) to recover theregional resistivity background values. The recoveredresistivities are shown in Figure 10a. When comparedwith the final model of Figure 8b (inversion for high-frequencies), one can note that the deep (below

Figure 9. Induction well log of W1 well at the reservoir inter-val showing high resistivity values (greater than 30 ohm-m)associated with the sandstone reservoir. Marls (MRL), sand-stones (SND), and shale (SHL).

Figure 10. (a) Line L1 Starting model and mesh of the2.5D polygonal inversion with a free 10 ohm-m initial guess forthe reservoirs vertical resistivity. The background model de-rives from prior 1D-CMP inversion. (b) Line L1 Final 2.5Dmodel with a 70 ohm-m recovered vertical resistivity for thereservoir corendered with the induction log of W1 well andthe seismic amplitude. The reservoir boundaries and regionalbackground resistivity were kept fixed in the inversion run.

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4800 m depth) resistive sediments (Albian carbonates)are better represented in the low-frequency invertedmodel of Figure 10a. This is, in turn, the starting modelfor the 2.5D polygonal inversion. We included in thismodel the thin reservoir geometry with an initial10 ohm-m resistivity (thin polygon body in Figure 10a).We assigned a fixed anisotropy ratio of 2 between thevertical and horizontal resistivities. This is an averagevalue calculated from several triaxial induction logmeasurements available in the Espirito Santo Basin.

We then inverted the inline electric field data of fivefrequencies at several runs with different reservoir (thinpolygon) extensions. At each run, for a given assumedreservoir boundary we kept fixed the background resis-tivities and then inverted only for the resistivity of thethin layer representing the reservoir. Figure 10a showsthe variable mesh design with finer discretization alongthe bathymetry and the thin layer, necessary for accu-rate calculations.

One advantage of the polygonal inversion approachis to quickly test different interpretation scenarios suchas varying the shape and size of the target, for instance.In the present case, the seismic interpretation predicteda smaller body than that provided by the CMP inversions.Therefore, we performed several robust tests with thepolygonal inversion to verify the extension of the reser-voir. All results consistently demonstrate the extensionof the reservoir northward as shown in Figure 10b,where a 70 ohm-m vertical resistivity is needed to fitthe data within a 10% error, as shown in Figure 11.

ConclusionsWe presented a fast-track mCSEM data interpreta-

tion workflow for hydrocarbon exploratory purposes.The main idea behind that workflow is to provide quickanswers to the asset teams and reduce the drilling risks.

The proposed in-house developed workflow consistsof three sequential steps: FR normalization, CMP, and2.5 polygonal inversions. In the first step, the goal isto identify mCSEM anomalies associated to reservoirsin depth. To that end, we applied the FR normalization

that mitigates the bathymetry effects in mCSEM data.After confirming the presence of the anomaly, CMP1D inversion is applied to the data to have a first glancein-depth positioning of the resistor, and provideregional background resistivity estimate that will beused as a priori information in the 2.5D polygonal inver-sion. In this last step, different interpretation scenarioscan be examined to confirm, or not, the presence of ahydrocarbon reservoir.

Petrobras successfully applied the proposed work-flow to several surveys acquired along the Brazilian off-shore margin. In the present paper, we showed anexample in a complex geologic area where the investi-gated prospect did not show a significative seismic AVOanomaly. The mCSEM inversion indicated the presenceof a resistor associated to the seismic target horizon.Two successful wells corroborated the mCSEM inter-pretation. The discovery is going now through appraisalphase, and more detailed mCSEM interpretation work-flows integrating well results, 3D seismic, and 3DmCSEM inversions are being applied.

AcknowledgmentsWe would like to thank Petrobras for its support and

permission to publish this paper. We are grateful to asso-ciate editor L. McGregor, M. Zhdanov, J. Nordskag, andan anonymous reviewer for their valuable comments andsuggestions that improved the present paper. We ac-knowledge A. Zerilli for his continuous enthusiasm andthe ongoing support in the application of the mCSEMmethod. We thank J. Lyrio for providing helpful sugges-tions for clarifying this work and T. Labruzzo for his sup-port to run the 2.5D inversion code. PTLM appreciatesthe support provided by a research grant from CNPq.

ReferencesAbubakar, A., T. Habashy, V. Druskin, D. Alumbaugh, A.

Zerilli, and L. Knizhnerman, 2006, Two-and-half-dimen-sional forward and inverse modeling for marine CSEM

Figure 11. Pseudosection of normalized mis-fit between real data and synthetic response(EdataEsynt 1) of the 2.5D polygonal inver-sion: (a) 1.4062 HZ and (b) 0.1562 HZ. Thesefrequencies are representative of the wholefrequency range.

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problems: 76th Annual International Meeting, SEG, Ex-panded Abstracts, 750754.

Abubakar, A., T. Habashy, M. Li, and J. Liu, 2009, Inversionalgorithms for large-scale geophysical electromagneticmeasurements: Inverse Problems, 25, 123012, doi: 10.1088/0266-5611/25/12/123012.

Asmus, H. E., and F. C. Ponte, 1973, The Brazilian marginalbasins, in A. E. M. Nairn, and F. G. Stehili, eds., Theocean basins and margins, vol. 1: Plenum Press, 87133.

Buonora, M. P., A. Zerilli, and T. Labruzzo, 2006, Marinecontrolled source electromagnetic: New insights in datainterpretation: Presented at SEG/SBGf D&P ForumDeep Water Challenges in Exploration, Development,and Production..

Buonora, M. P. P., R. Reddig, W. Heelan, J. D. Schofield, A.Zerilli, and T. Labruzzo, 2005, Some preliminary inter-pretation on the marine controlled source electromag-netic (MCSEM) data acquired on Campos Basin, Brazil:Presented at 9th International Congress of the BrazilianGeophysical Society.

Cainelli, C., and W. U. Mohriak, 1999, Some remarks onthe evolution of sedimentary basins along the EasternBrazilian continental margin: Episodes, 22, 206216.

Cobbold, P. R., P. Szatmari, L. S. Demercian, D. Coelho,and E. A. Rossello, 1996, Seismic and experimental evi-dence for thin-skinned horizontal shortening by conver-gent radial gliding on evaporites, deep-water SantosBasin, Brazil, in M. P. A. Jackson, D. G. Roberts, andS. Snelson, eds., Salt tectonics: A global perspective:AAPG Memoir 65, 305321.

Commer, M., and G. A. Newman, 2008, New advances inthree-dimensional controlled-source electromagneticinversion: Geophysical Journal International, 172,513535, doi: 10.1111/j.1365-246X.2007.03663.x.

Constable, S., 2010, Ten years of marine CSEM for hydro-carbon exploration: Geophysics, 75, no. 5, A67A81,doi: 10.1190/1.3483451.

Constable, S., and L. J. Srnka, 2007, An introduction tomarine controlled-source electro-magnetic methodsfor hydrocarbon exploration: Geophysics, 72, no. 2,WA3WA12, doi: 10.1190/1.2432483.

de Lugao, P. P., S. L. Fontes, E. F. La Terra, A. Zerilli, T.Labruzzo, and M. P. Buonora, 2008, First applicationof marine magnetotellurics improves depth imaging inthe Santos Basin, Brazil: 70th Annual InternationalConference and Exhibition, EAGE, Extended Abstracts,P192.

Eidesmo, T., S. Ellingsrud, L. M. MacGregor, S. Constable,M. C. Sinha, S. E. Johansen, F. N. Kong, and H.Westerdahl, 2002, Sea bed logging (SBL), a new methodfor remote and direct identification of hydrocarbonfilled layers in deepwater areas: First Break, 20, 144152.

Ellingsrud, S., T. Eidesmo, S. Johansen, M. Sinha, L.MacGregor, and S. Constable, 2002, Remote sensing ofhydrocarbon layers by seabed logging (SBL): Results

from a cruise offshore Angola: The Leading Edge, 21,972982, doi: 10.1190/1.1518433.

Foster, D. J., R. G. Keys, and F. D. Lane, 2010, Interpreta-tion of AVO anomalies: Geophysics, 75, no. 5, A3A13,doi: 10.1190/1.3467825.

Gallardo, L. A., S. L. Fontes, M. A. Meju, M. P. Buonora, andP. P. de Lugao, 2012, Robust geophysical integrationthrough structure-coupled joint inversion and multi-spectral fusion of seismic reflection, magnetotelluric,magnetic, and gravity images: Example from Santos Ba-sin, offshore Brazil: Geophysics, 77, no. 5, B237B251,doi: 10.1190/geo2011-0394.1.

Habashy, T. M., and A. Abubakar, 2004, A general frame-work for constraint minimization for the inversion ofelectromagnetic measurements: Progress in Electro-magnetics Research, 46, 265312, doi: 10.2528/PIER03100702.

Hart, W., and M. Albertin, 2001, Subsalt trap archetypeclassification: A diagnostic tool for predicting and pri-oritizing Gulf of Mexico subsalt traps, in R. H. Fillon,N. C. Rosen, and P. Weimer, eds., 21st Annual Bob F.Perkins Research Conference, Petroleum Systems ofDeep-Water Basins: Global and Gulf of Mexico Experi-ence: GCSSEPM Foundation, Abstracts, 619638.

Hoversten, G. M., S. C. Constable, and H. F. Morrison, 2000,Marine magnetotellurics for base-of-salt mapping: Gulfof Mexico field test at the Gemini structure: Geophysics,65, 14761488, doi: 10.1190/1.1444836.

Li, Y., and S. Constable, 2007, 2D marine controlled-sourceelectromagnetic modeling: Part 2 The effect ofbathymetry: Geophysics, 72, no. 2, WA63WA71, doi:10.1190/1.2430647.

Lorenz, L., H. T. Pedersen, and M. P. Buonora, 2013, Firstresults from a Brazilian mCSEM calibration campaign:Presented at 13th International Congress of the Brazil-ian Geophysical Society.

Lumley, D. E., 2001, Time-lapse seismic reservoir monitor-ing: Geophysics, 66, 5053, doi: 10.1190/1.1444921.

Matias, H., P. Kress, P. Terrinha, W. Mohriak, P. T. L.Menezes, L. Matias, F. Santos, and F. Sandnes, 2011, Salttectonics in the western Gulf of Cadiz, southwest Iberia:AAPG Bulletin, 95, 16671698, doi: 10.1306/01271110032.

Menezes, P. d. T. L., and J. M. Travassos, 2005, EM model-ing of the central-northern portion of Ponta Grossa Arch,Paran Basin, Brazil: Physics of the Earth and PlanetaryInteriors, 150, 145158, doi: 10.1016/j.pepi.2004.08.031.

Menezes, P. d. T. L., and E. R. Morais, 2003, Shallowelectromagnetic exploration for hydrocarbons atSergipe-Alagoas Basin, Brazil: The Leading Edge, 22,11271129, doi: 10.1190/1.1634917.

Menezes, P. T. L., and J. M. Travassos, 2010, Magnetotel-lurics as a modeling tool in the extensive magmaticcontext of Paran Basin, Brazil: The Leading Edge,29, 832840, doi: 10.1190/1.3462787.

Milani, E., J. Brando, P. Zaln, and L. Gamboa, 2000,Petrleo na margem continental brasileira: Geologia,

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explorao, resultados e perspectivas: Revista Brasi-leira de Geofsica, 18, 352396, doi: 10.1590/S0102-261X2000000300012.

Mohriak, W. U., P. Szatmari, and S. Anjos, 2012, Salt: Geol-ogy and tectonics of selected Brazilian basins in theirglobal context: Geological Society London, Special Pub-lications, 363, 131158, doi: 10.1144/SP363.7.

Plessix, R.-E., and W. Mulder, 2008, Resistivity imagingwith controlled-source electromagnetic data: Depthand data weighting: Inverse Problems, 24, 034012,doi: 10.1088/0266-5611/24/3/034012.

Silva Crepaldi, J. L., M. P. Pereira Buonora, and I. Figueir-edo, 2011, Fast marine CSEM inversion in the CMPdomain using analytical derivatives: Geophysics, 76,no. 5, F303F313, doi: 10.1190/geo2010-0237.1.

Smith, R., and J. Rowe, 1997, A new regional explorationmethod for detecting hydrocarbon alteration plumes:The ALTREX method: Exploration Geophysics, 28,286291, doi: 10.1071/EG997286.

Strack, K., 2014, Future directions of electromagneticmethods for hydrocarbon applications: Surveys in Geo-physics, 35, 157177, doi: 10.1007/s10712-013-9237-z.

Travassos, J. M., and P. T. L. Menezes, 1999, Geoelectricstructure beneath limestones of the Sao Francisco Ba-sin, Brazil: Earth Planets Space, 51, 10471058.

Volozh, Y., C. Talbot, and A. Ismail-Zadeh, 2003, Salt struc-tures and hydrocarbons in the Pricaspian Basin: AAPGBulletin, 87, 313334, doi: 10.1306/09060200896.

Wilson, L. J., S. C. Brian, D. R. Weaver, M. E. Cribbs, and A.W. Leibold, 2002, Champlain: A subsalt discovery in thedeepwater Gulf of Mexico: Presented at the 87th AAPGAnnual Meeting.

Zerilli, A., M. P. Buonora, A. Abubakar, and T. Labruzzo,2011, Inversion of a marine controlled source electro-magnetic data using a structure-based approach: Pre-sented at 12th International Congress of the BrazilianGeophysical Society.

Zerilli, A., T. Labruzzo, M. P. Buonora, P. de Tarso Luiz Me-nezes, and A. Lovatini, 2010, 3D inversion of total fieldmCSEM data: The Santos Basin case study: 80th AnnualInternational Meeting, SEG, Expanded Abstracts, 629633.

Zerilli, A., A. Lovatini, A. Battaglini, and P. D. L. Menezes,2012, Resolving complex overthrust features usingmagnetotellurics The Bolivian foothills case study:Presented at First EAGE/ACGGP Latin American Geo-physics Workshop.

Marco Polo Pereira Buonora re-ceived a B.S. in geology in Recife,Pernambuco, Brazil. He received a Ful-bright scholarship in 1974 and receivedmaster's and Ph.D. degrees in geophys-ics with emphasis on gravity and mag-netics from St. Louis University. He isthe manager of nonseismic geophysicsat Petrobras in Rio de Janeiro, Brazil.

He is responsible for the acquisition, processing, and inter-pretation of all nonseismic data gravity, magnetic, andEMmethods, particularly mCSEM. He has been based in Riode Janeiro since 1969. He began his career as a geologist atthe Brazilian Ministry of Mines and Energy, working withgravity and magnetics data acquisition and interpretation,and later he worked for a mining company looking for basemetals and gemstones and as a geophysicist in a servicecompany focused on acquisition, processing, and interpreta-tion of airborne-magnetics and gammaspectromety data.After returning to Brazil in 1980, he joined Petrobras wherehe has been active in gravity and magnetic interpretation ofseveral onshore and offshore sedimentary basins in Brazil.He has worked in the areas of vertical seismic profiling, onland and offshore, and nuclear magnetic resonance. Overthe past 10 years, he has been involved with the acquisition,processing, and interpretation of mCSEM, and he was theSEG 2013 Central and South America Honorary Lecturer.He is also a part-time associate professor at FluminenseFederal University, in Niteroi, Brazil, where he teaches ap-plied gravity, magnetics, digital signal analysis, and inversionof geophysical data. He is a member of SEG, EAGE, and theBrazilian Geophysical Society, serving as president 19891991.

Jorlivan L. Correa received a B.S.(2010) in geophysics from UFPA. Hehas been working with electromag-netic methods since his time at univer-sity and has been a geophysicist atPetrobras since 2012. His main inter-est is integration of geophysical meth-ods for better interpretation.

Luciano dos Santos Martins re-ceived a B.A. (2010) in physics fromthe Federal University of Campinas.He began his career as a geophysicistin 2011 at Petrobras, working withmCSEM data. His main interests arein integration of seismic and mCSEMinterpretation.

Paulo T. L. Menezes received aB.S. (1986) in geology from Rio deJaneiro State University, Brazil, anM.S. (1990) in geophysics from theFederal University of Par, Brazil,and a Ph.D. (1996) in geophysics fromthe National Observatory of Rio de Ja-neiro. In 19961997, he worked as aconsultant at Geomag Aerolevanta-

mentos Ltda. Since 1997, he has been a professor at Riode Janeiro State University. He also works as a geophysi-cist at Petrobras. His research interests include interpreta-tion of potential-field, seismic, and electromagnetic data.He is a member of SEG, EAGE, and the Brazilian Geophysi-cal Society.

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Emanue J. C. Pinho received a de-gree (2002) in physics and a Ph.D.(2006) in cosmology. He joined thePetrobras EM group in 2006. His maininterests are 2D and 3D EM modelingand inversion.

Joo Lucas Silva Crepaldi receiveda B.A. (2005) in physics from theFederal University of So Carlos in2005. He started his masters degreeat USP So Carlos with an emphasison quantum information. He completedhis masters degree at the NationalObservatory in electromagnetic data in-version. He is a geophysicist expert in

electromagnetic methods at Petrobras.

Mirela P. P. Ribas received a B.S.(2006) in geology from the Universityof Braslia. Between 2006 and 2009,she worked as a mineral explorationgeologist at TeckCominco S/A. Since2009, she has worked as a geophysi-cist at Petrobras, in interpretation ofpotential field and electromagneticdata. Her main interests are in integra-

tion of geophysical data to regional studies.

Sergio M. Ferreira received a B.S.(2007) in geology from the Universi-dade Federal do Rio de Janeiro andan M.S. (2011) in geophysics fromthe Observatorio Nacional do Rio deJaneiro, Brazil, where his researchwas focused on gravimetric dataanalysis. He is a reviewer for Com-puters & Geosciences journal and is

a member of the Brazilian Geophysical Society. He hasbeen working at Petrobras since 2008, as part of the EMteam, where he has been developing expertise on the con-trolled-source EM method.

Rafael Correia de Freitas receivedamaster's degree (2005) in basin analy-sis from the Universidade Federal doParan, working with structural geol-ogy and geomathematics, integratingsurface and subsurface data. He hasbeen a geologist and seismic inter-preter at Petrobras since 2006. Hiswork is focused in structural geology

and data integration applied to the definition and evalu-ation of exploratory areas. Currently, he is working inEsprito Santo Basin (offshore Brazil), with subsalt oppor-tunities.

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