multiphysics anomaly map results for pre-salt block bmc-33

Multiphysics anomaly map results for pre-salt block BMC-33, Campos Basin, Brazil Julio C. S. O. Lyrio, Petrobras – EXP/GEOF/MNSAna Patricia R. Q. A. Santana, Petrobras – EXP/GEOF/IAG

Copyright 2019, SBGf - Sociedade Brasileira de Geofísica

This paper was prepared for presentation during the 16 th International Congress of theBrazilian Geophysical Society held in Rio de Janeiro, Brazil, 19-22 August 2019.

Contents of this paper were reviewed by the Technical Committee of the 16 th

International Congress of the Brazilian Geophysical Society and do not necessarilyrepresent any position of the SBGf, its officers or members. Electronic reproduction orstorage of any part of this paper for commercial purposes without the written consentof the Brazilian Geophysical Society is prohibited.______________________________________________________________________

Abstract

The Campos Basin, in southeast Brazil, wherefractured basalts has been identified as oil reservoirs, isan example of the hydrocarbon exploratory potential inigneous rocks. Recently, new basalt flows in the sectionbelow salt was identified as potential reservoirs in theblock BMC-33. Because geophysical interpretation insuch scenario is ambiguous and complex, theidentification and mapping of such rocks is usuallydifficult. In such context, geophysical data integration hasbeen largely applied to reduce exploration risk. Theintegration of interpretation results from multiplegeophysical methods in the recently proposedmultiphysics anomaly map is a methodology that hasproved to be an efficient qualitative tool for a wide rangeof geological problems. In this methodology, geophysicaldata from different methods are mathematically treatedwith a logistic function, a function specially designed tobounds large values, to allow reliable combination. Theprocess emphasizes regions where individualinterpretations from different geophysical methodscorrelate, which represent large probabilities of targetoccurrence. The effectiveness of the multiphysicsanomaly map in block BMC-33 becomes evident from thecorrect identification of volcanic rocks sampled in thewells but also from the capacity of providing qualitativeestimate of the relative thickness of these volcanic rocks.

Introduction

The occurrence of igneous rocks in the Brazilianoffshore sedimentary basins is consequence of magmaticevents that have succeeded the rifting and theconsequent South America-Africa continental separation.In this context, the Campos Basin, where fracturedbasalts has been identified as oil reservoirs, is a goodexample of the hydrocarbon exploratory potential of theigneous rocks.

Recently, three new drilled wells have sampledvolcanic rocks in the pre-salt section of the BMC-33 block,in Campos Basin (Figure 1). Although two of the wells(REPF-11 and REPF-12) have sampled non-reservoirbasaltic flows, the REPF-14 well sampled a potentialhydrocarbon reservoir volcanic rocks. In the presence ofsuch scenario, where volcanic rocks can change fromnon-reservoir to reservoir in short distances, the

geophysical mapping of such features becomes achallenge to the interpreter (Santana et al, 2018).

Besides the large variability of these volcanic rocksin the region, the ambiguity inherent to the geophysicalinterpretation makes the identification and mapping ofsuch rocks complex. The integration of the interpretationresults from multiple geophysical methods in the recentlyproposed multiphysics anomaly map (Lyrio et al, 2019),has proved to be an efficient tool to effectively combinethe different outputs bringing confidence to theinterpretation.

Multiphysics anomaly map (Lyrio et al, 2019) is adata fusion solution for geophysical data combinationwhere seismic attributes, gravity and magnetic data, forinstance, are mathematically processed to allow reliablecombination. In each method, anomalous regions ofinterest are highlighted through the use of a logisticfunction. Logistic function is a mathematical functionspecially designed to clip large magnitudes and bound therange of values, which allows maximum discriminationamong the data.

In this process, anomalies showing spatialsuperposition will be emphasized while the remaininganomalies tend to be diminished. The resulting anomaliesare then combined in the multiphysics anomaly map,which represents regions of large probabilities of target-occurrence. We applied the multiphysics anomaly mapsolution to the real problem of identifying and mapping thevolcanic rocks in the pre-salt block BMC-33.

Method

Seismic data interpretation in the area is complexand ambiguous since some volcanic rocks identified inthe wells show similar seismic responses to reservoir andnon-reservoir rocks. In order to reduce both ambiguityand complexity of the interpretation, it is necessary tointegrate the different geophysical methods available inthe area. Among several seismic attributes available, thediscontinuity attribute (Figure 2a) was chosen as the bestto define the lateral distribution of the volcanic rocks(Santana et al, 2018).

The seismic discontinuity attribute (Figure 2a)measures the degree of local similarity between seismictraces. The low similarity values, characterized by darkcolors, are believed to bound the lateral extension of thevolcanic rocks that were interpreted as lava fans andvolcanic craters.

In addition to the seismic attribute, it was alsoavailable potential field data like gravity and magnetics. Aresidual gravity anomaly map calculated to focus on thevolcanic rocks depth (Figure 2b) shows strong anomalous

Sixteenth International Congress of the Brazilian Geophysical Society

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MULTIPHYSICS ANOMALY MAP FOR BMC-33, CAMPOS BASIN___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

values, usually associated to high density sources wereinterpreted as the gravity responses from the igneousrocks. Similarly, strong anomalous values were also foundin the analytic signal amplitude map of the magnetic data(Figure 2c). Such anomalous magnetic values wereinterpreted as the magnetic responses of volcanic rocksunderneath.

Data combination from different geophysicalmethods usually requires normalization to dealappropriately with the distinct physical units. Normalizingdata by dividing it by a chosen constant value is acommon practice. However, its linear nature can createlarge results for bounding values of the dataset, whichmay lead to undesirable results during anomalycombination. In order to prevent such undesirable resultswe decided to work with the logistic function.

A logistic function is a common non-linearmathematical function showing a typical sigmoid curveoften used in statistics and machine learning techniques.In this approach, we use a modified version of the logisticfunction specially designed to clip large magnitudes andbound the range of values (Figure 3). The generic form ofthe logistic function σ proposed in this work can bedescribed by the equation:

σ (x )=L

1+e−ks (x−x 0)

,

where e is the natural logarithm base, x0 is the x-value ofthe sigmoid inflection point, L is the curve maximumvalue, k is the steepness of the curve, and s is a scalingfactor that fits the data domain into the chosen functiondomain. The asymptotic characteristic of the logisticfunction as x tends to infinity seems more suitable forgetting the desired normalization results since evenextreme x-values will always result in σ-values boundedby the logistic function range.

In the multiphysics anomaly map approach eachgeophysical method needs to be appropriately scaled tothe (-24,24) interval, chosen as the function domain. Eachdataset have its domain shifted and centered to zerobefore being rescaled by the scale factor s. The scalefactor s is the ratio between the domain of the logisticfunction and the data domain.

The choice of the parameter x0 is of fundamentalimportance and is, indeed, an interpretation task becausethe selection of significant anomalies in each method isproblem dependent. The selection of x0 must be madewith care since it is critical for the final results. Theresulting datasets after applying the logistic function toeach anomalous data are named logistic anomalies.

The logistic anomalies calculated for thegeophysical data in Figure 2 are presented in Figure 4.The parameters L = 2 and k = 0.25 were kept constant forall three geophysical data transformation. The seismiclogistic anomaly in Figure 4a was obtained by applying ascale factor s = 510.64 and x0 = 0.007 to the seismicdiscontinuity data in Figure 2a. The gravity residual ofFigure 2b was processed with scale factor s = 41.38 andx0 = 0.22 to produce the gravity logistic anomaly show inFigure 4b. Finally, scale factor s = 4000 and x0 = 0.003were used to transform the analytic signal amplitude data

of Figure 2c into the magnetic logistic anomaly in Figure4c.

The fusion of all logistic anomalies into themultiphysics anomaly map is accomplished by multiplyingall logistic anomalies. As a result, the multiphysicsanomaly map highlights regions showing the maximumcorrelation between major features interpreted fromdifferent geophysical methods. In the multiphysicsanomaly map, we expect areas showing larger values torepresent greater probabilities of target occurrence.

Results

The three logistic anomalies shown in Figure 4 weremultiplied together to composed the multiphysics anomalymap exhibited in Figure 5. The computed multiphysicsanomaly map shows anomalous regions in the vicinity ofthe wells REPF-14 and REPF-12, where volcanic rockswere sampled by the wells. Although volcanic rocks havealso been identified in REPF-11, no anomaly wasdetected in this position because the volcanic rocks inREPF-11 are located below the interval were thegeophysical data processing was focused.

The amplitude of the anomalous values in themultiphysics anomaly map shows a reduction in intensityfrom well REPF-14 to REPF-12. The amount ofvolcanoclastic rocks in REPF-14 (orange color in the welllog of Figure 5) associated with the summation of the lavaflows thickness in REPF-12 (red colors in the well log ofFigure 5) and the absence of the igneous rocks in the welllog of REPF-11, strongly correlates with the totalthickness of volcanic rocks measured in each well.Therefore, the multiphysics anomaly map of block BMC-33 was interpreted as a qualitative indicator of thevolcanic rocks volume in the area.

Conclusions

The integrated interpretation of geophysical dataperformed in the block BMC-33, based on a recentlydeveloped multiphysics anomaly map methodology thatcombines seismic attributes and potential fields maps,has contributed to the understanding of the volcanic rocksdistribution in the area.

The presence of multiphysics anomalies in theregions where wells have detected volcanic rocks bringsconfidence to extend such interpretation to other areas inthe block BMC-33 as well as in nearby blocks showingsimilar geological context. The strong correlation betweenthe anomaly intensity in the multiphysics anomaly mapand the volcanic rocks volume measured in the wellshighlights the value of the applied methodology as aqualitative tool to estimate the relative volume of this typeof rocks.

The incorporation into the multiphysics anomalymap of structural and stratigraphic features, for instance,should amplify its usefulness as integrated interpretationtool and contribute to the expansion of its multimodalnature.


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LYRIO,J. C. S. O. AND SANTANA, A. P. R. Q. A.___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

Acknowledgments

The authors would like to thank Petrobras forpermission to publish this work.

References

Lyrio, J. C. S. O., Menezes, P. T. L., Correa, J. L., Viana,A. R., 2019. Multiphysics anomaly map: a new data fusionworkflow for multiphysics interpretation. Under review inInterpretation.

Santana, A.P.R.Q.A., Lyrio, J. C. S. O., Pimentel, A.L.,Bevilaqua, L.A., Oliveira, C.M.M., Kolisnyc, A., Marins,G.M., Santos, M.N., Pintas, E.M., Dias, B.S.S., 2018.Avaliação e Integração de dados de perfil, rocha, sísmicae métodos potenciais aplicada ao mapeamento einterpretação qualitativa de derrames basálticos, naseção rift do Pré-sal. Anais do 49º Congresso Brasileiro deGeologia, Resumo STV3-7813.


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Figure 1 – Location of study area. The red box shows the position of block BMC-33 in Campos Basin, Brazil.

Figure 3 – Example of logistic functions. In dashed lineis the standard sigmoid function while the modifiedlogistic function is shown by the solid line.



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(a) (b)

(c)

Figure 2 – Anomalies selected for construction of the multiphysics anomaly map in block BMC-33, Campos Basin, Brazil. In the seismic discontinuity in (a) the dark areas were interpreted as indicative of volcanic rocks. The gravity residual in (b) shows positive anomalies, usually related to high density rocks, which can be indicate presence of igneous rocks. The strongmagnetic anomaly in the amplitude of the analytic signal in (c) may also represent the response of volcanic rocks.

LYRIO,J. C. S. O. AND SANTANA, A. P. R. Q. A.___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


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(a) (b)

(c)

Figure 4 – Logistic anomalies calculated applying the proposed modified logistic function to the selected geophysicalanomalies shown in Figure 3. In all three results, the logistic seismic anomaly shown in (a), the logistic gravity anomaly in (b)and the logistic magnetic anomaly in (c), the higher values represent the regions with large probability of occurrence ofvolcanic rocks.



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Figure 5 – The multiphysics anomaly map resulting from the fusion of the three logistic anomalies shownin Figure 4. The position of the main anomalies correspond to the presence of volcanic rocks sampled bythe wells. The amount of volcanoclastic rocks in REPF-14 (orange color in the well log) associated withthe summation of the lava flows thickness in REPF-12 (red colors in the well log) and the absence of theigneous rocks in the well log of REPF-11, makes clear that the intensity of the anomalous values directlycorrespond to the total thickness of volcanic rocks in each well.

multiphysics anomaly map results for pre-salt block bmc-33

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