The application of seismic attributes for reservoir characterizationin Pre-Tertiary fractured basement, Vietnam-Malaysia offshore

Nguyen Huy Ngoc1, Sahalan B. Aziz1, and Nguyen Anh Duc2

Abstract

The Pre-Tertiary fractured basement forms important hydrocarbon-bearing reservoirs in the Vietnam-Malaysia offshore area, and is being produced from such reservoirs in Vietnam where the authors have exten-sive working experiences for both clastics and fractured basement reservoirs and in both exploration and de-velopment phases. Due to their very small matrix porosity, the basement rocks become reservoirs only whenthey are strongly fractured. The quality of the fractured basement reservoirs depends on basement rock type,fracture density, and fracture characteristics including aperture, azimuth, dip, continuity, and fracture systemintersection. Three-dimensional seismic data is applied widely to characterize these basement reservoirs. Basedon results from applying many different seismic attributes to 3D seismic data from different Pre-Tertiary frac-tured basements in Vietnam and Malaysia, we demonstrate the utility of attributes in characterizing fracturedbasement reservoirs. Seismic attributes help predict the basement rock type and fracture characteristics fromnear top basement to deep inside basement. In the zone near the top of basement, the characteristics of fracturesystems can be predicted by amplitude, coherence, curvature, and secondary derivative attributes. Deep insidethe basement, relative acoustic impedance and its attributes have been successfully applied to predict the dis-tribution of high fracture density, while dip and azimuth, ant-tracking, and gradient magnitude attributes haveproven to be effective for predicting fracture characteristics. The accuracy of fracture characterization based onseismic attributes has been verified by drilling results.

IntroductionPre-Tertiary fractured basement (referred to below

as “fractured basement”) is becoming an importanttype of hydrocarbon-bearing reservoir in the Vietnam-Malaysia offshore area, which encompasses a seriesof Tertiary petroleum basins (Figure 1). In this region,the basement varies in rock type and age. In thenorth, the basement of the Song Hong Basin offshoreVietnam mainly consists of Paleozoic metasedimentand carbonates rocks (Nielsen et al., 1999). In thecenter, the basements of the Cuu Long Basin offshoreVietnam mainly comprise Mesozoic granite (Trinh,2008). In the south, the basement of the Malay Basinoffshore Malaysia is a combination of Mesozoicand Paleozoic metasediment, carbonate, and igneousrocks (Madon, 1999). In some areas, the basementrocks are cut by younger dikes of dolerite or andesite,which are probably of Tertiary age. In the study area,the typical basement trap type is a basement highformed by a Horst block, buried hill, or pop-up struc-tures, covered and juxtaposed by thick shale thatplays the dual role of seal and source rock (Ngocet al., 2011).

In the last decade, by application of state-of-the-art seismic technology, methods of 3D seismic dataprocessing such as beam prestack depth migration havesignificantly improved the seismic imaging of fracturezones associated with faults within the basement —

even for narrow azimuth seismic data, which so farare the only available seismic data in the study region(Figure 2). This, in turn, enables seismic attributes to beapplied widely throughout the region to identify andoutline good fracture zones inside the basement andto predict some of the main characteristics of theexisting fracture systems, such as azimuth and dip.Some results for the Cuu Long Basin have been pre-sented previously by Nguyen et al. (2010) and Ngocet al. (2011). Here, we present results not only forthe Cuu Long Basin, but also for the Song Hong Basinto the north and Malay Basin to the south, whichhave different basement rock types. Recent results fromdrilling, logging, and testing prove the certainty and ac-curacy of our fracture prediction derived through seis-mic attributes.

It is necessary to emphasize that surface seismic datacan image only fracture zones associated with a certain

1PCSB, Kuala Lumpur, Malaysia. E-mail: nguyen.huy@petronas.com.my; sahalan@petronas.com.my.2PVEP, Hanoi, Vietnam. E-mail: ducna@pvep.com.vn.Manuscript received by the Editor 11 June 2013; revised manuscript received 15 September 2013; published online 12 February 2014. This paper

appears in Interpretation, Vol. 2, No. 1 (February 2014); p. SA57–SA66, 16 FIGS.http://dx.doi.org/10.1190/INT-2013-0081.1. © 2014 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.

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Special section: Seismic attributes

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fault or fault system, but it cannot image individual frac-tures. Hence, we discuss characteristics of faults orfracture systems but not those of individual fractures.

Factors influencing fracturedbasement reservoir quality

The quality of fractured basement reservoirs is deter-mined by a combination of six geologic factors, whichare described in detail in Ngoc et al. (2011), and are asfollows.

• Basement structural styleBasement high with popup style resulted mainlyfrom compression tectonic has more fracturesthan other structural styles. Basement buried hills

are characterized by fewer fractures than otherstructures. Elongated basement highs are betterfractured than isometric structures.

• Basement rock typeWithin the same tectonic regime, brittle and solidrocks are more fractured than ductile and layeredrocks. Therefore, different basement rock typesexhibit different fracture intensity and fracturecharacteristics. Based on global statistics givenin Sircar (2004) and Batchelor and Gutmanis(2012), it can be said that in general granite base-ment has much better fracture properties thanmetamorphic rocks, and high-grade metamorphicbasement has better fracture properties than low-grade metamorphic basement.

• Fracture densityThe quality of fractured basement dependsgreatly on the fracture density, especially the den-sity of open fractures.

• Continuity and intersection of different frac-ture systemsThe maintenance of the flow rate and pressure infractured basement reservoirs is determined bythe continuity and the intersections of differentfractured systems.

• Aperture of open fracturesThe flow rate capacity of fractured basement res-ervoirs depends on the number and aperture oflarge open fractures.

• Existence of young volcanic dikesThe existence of young volcanic dikes in principlereduces the quality of fractured basement reser-voirs. However, in the Malay Basin where themain type of basement rock type is metasedimen-tary, a high fracture density in the dikes is ob-served in core and well logging data.

Industry experience gained from the exploration andproduction of hydrocarbons from fractured basementFigure 1. Location map of study area.

Figure 2. Example showing how appliedprocessing sequence and processing parame-ters can degrade data quality inside basementimaging. A and B for granite basement in CuuLong Basin, C and D for granite basement inthe Malay Basin.

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reservoirs in the study area confirms that reservoirquality is highly localized. In the same large basementstructure, wells drilled only a few hundred meters apartfrom each other can have quite different results. There-fore, fractured basement reservoir characterization isvery important at all stages, from exploration untilthe end of life of the field.

Seismic attributes for characterizing fracturedbasement reservoirs

To characterize fractured basement reservoirs, seis-mic attributes must be able to

• predict basement rock type distribution,• enhance the detail and accuracy of basement fault

interpretation,• predict high fracture density distribution inside

the basement, and• characterize the fracture systems including dip,

azimuth, continuity, and intersection.

The quality of seismic imaging within the basement isstrongly affected by the data acquisition and processingparameters. Thus, the first key step in basement seismicinterpretation is to assess the imaging quality of theseismic data to select the best data set for further inter-pretation. When only narrow azimuth 3D seismic dataare available, as is the case currently in our study area,assessing the quality of the seismic imaging producedby different processing sequences, especially methodsof seismic migration, becomes more important. In somecases, additional poststack processing is required to en-

hance the signal to noise ratio for the selected data. Theadditional poststack processing could include struc-tural smoothing, integrated trace, frequency and f–kfiltering, relative acoustic impedance (AI), and, if nec-essary, even data restacking. Figure 2a and 2b illus-trates the importance of this quality control step byshowing that quite different images of the basementcan be derived from the same vintage of 3D seismicdata, in this case by applying PSTM and control beamPSDM processing sequences. Even applying 3D PSTMprocessing to create near-offset and full-offset stackeddata yields significant differences in basement imaging,as seen in Figure 2c and 2d. They clearly show that thenear offset stacked data have much better, higher fre-quency reflections from basement objectives comparedto the full-offset stacked data.

Different basement lithologies have different AIswhich in turn affect seismic reflection intensities. Asa result, a combination of the wave picture and seismicamplitude attributes can be used to separate differentrock types in the basement. Figure 3 shows a clear dis-tribution of granite basement, which is confirmed bywell data, separated by metasedimentary basement,which are predicted by using wave picture characteris-tics and the sum of the positive amplitudes of the base-ment top reflection. In another area, a seismic reflectionmagnitude attribute, shown in Figure 4, identifies thedistribution of thick dolerite dikes within the granitebasement. The existence of the dikes is confirmed bydrilling. Figure 5 shows the ability of the envelope ofthe relative AI attribute to separate the metasedimen-tary and granite basement in the Malay Basin. The

Figure 3. Sum positive amplitude map (timewindow of 24 ms) at the basement top showingdifferent rock types inside the basement. Ex-ample from the Malay Basin.

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Figure 4. Reflection magnitude attributemap at different levels (275 and 550 m) belowthe basement top showing existing of thickdikes (violet) inside the granite basement. Ex-ample from the Cuu Long Basin.

Figure 5. Seismic imaging and seismic attrib-utes for metasedimentary, possibly low-grademetamorphic (section between red and yellowhorizons) and granite basement (section belowthe yellow horizon) in the Malay Basin.

Figure 6. Seismic attribute maps of near topbasement (time window of 40 ms) showingpossible good and poor fracture zones in thebasement. Example from the Malay Basin.

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granite basement is confirmed by well C-2 and themetasedimentary basement is penetrated by wells in ad-jacent areas.

Basement rocks are much older than their overlyingsedimentary sequences, and they have been affected byall the tectonic events that happened before and duringthe formation of the overlying sedimentary basin. As aconsequence, basement faults are very complicated andconsist of different systems that have different faulttypes and different orientations, and which wereformed at different geologic times (Ngoc et al., 2010).In addition to visualizing the top basement in 3D, seis-mic attributes such as coherence, secondary derivativeof the basement top, and curvature help greatly by im-proving the detail and fault identification and correla-tion at the basement top (Ngoc et al., 2010, 2011).Combining these seismic attributes with relative AIand ant-tracking attribute cubes to track basement

faults allows the data to be interpreted with a high levelof detail, certainty, and accuracy. The existence of frac-tures in the zone closed to the top of basement affectsreflections from the top of basement. Therefore, attrib-utes of the basement top can help predict the distribu-tion of the high fracture density in the topmost part ofthe basement. Figure 6 shows an example of this, withseveral seismic attributes, including sum of positive am-plitude, variance, and maximum curvature, that helpseparate highly fractured from other zones near thebasement top.

Figure 7d shows AI probability density functions(PDF) of fractured granite, fresh granite, and dikeswhich were constructed by well logging data from threebasement wells in field A in the Cuu Long Basin. It isclear that good fracture zones inside the basement havelower AI compared to unfractured zones and dikes.Therefore, relative AI attribute, beside having a higher

Figure 7. Some characteristics of fracturedgranite basement in Cuu Long Basin: 3D PSDMseismic section (A), relative AI (B), relative AIand fracture porosity (C), AI PDFs of granitebasement.

Figure 8. Comparison between azimuths ofinside basement fracture zones interpretedby relative AI (color lines with dots) andFMI data interpretation results for four frac-ture zones penetrated by Well A-5 (Cuu LongBasin). The red circles are locations of thewell penetrated time slice levels. The rose dia-grams show azimuths of faults (violet), con-ductive continue fractures (dark blue), anddiscontinue fractures (yellow) at respectivelevels.

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signal-to-noise ratio compared with the original seismic(Figure 7a and 7b), can be a good tool to predict highfracture density distribution inside the basement (Fig-ure 7c). The good consistency between fracture poros-ity calculated by well logging data and negative relativeAI, seen in Figure 7c, confirms the effectiveness of thisattribute. Figure 8 shows good consistency between di-rections of interpreted faults, which are fracture zones,at different relative AI time slices and azimuths of frac-tures and faults at respective locations for the four frac-tured zones inside the granite basement penetrated bythe well A-5 of oil field A in Cuu Long Basin. In additionto relative AI, the reflection intensity of the gradientmagnitude is also a good seismic attribute for imaginghigh fracture density zones inside basement (Figure 9).Figure 10 presents sections of the gradient magnitude ofrelative AI along trajectories of two basement explora-tion wells in the Malay Basin, wells A-2 and B-2. Itshows that well B-2 did not penetrate any seismic attrib-ute anomaly and also did not penetrate any good frac-

ture zone, whereas well A-2 penetrated many seismicattribute anomalies and tested with oil flow from multi-ple zones.

To characterize different fracture systems by dip,azimuth, intersection, and continuity, the followingseismic attributes have proven most effective: 3D seis-mic dip (Figure 11), dip and azimuth of P-impedance(Figure 12), gradient magnitude of relative AI, andant-tracking of relative AI (Figure 13). The last attributeis also good for showing the intersection between dif-ferent fracture systems in time/depth slices and verticalsections (Figure 13). The good consistency between in-tersections of different fracture systems predicted byant-tracking and the well results for the granite base-ment is clearly demonstrated in Bone and Giang(2008). Figure 12 shows dip and azimuth attribute mapsat different levels below the carbonate basement top inthe Song Hong Basin, and shows the location of the twofirst basement exploration wells in the area. Drilling re-sults, including drilling breaks and drilling mud lost,

Figure 9. Seismic attribute maps (A, B, andC) at 250 m below the basement top and sec-tion-ef (D) showing distribution of possiblegood fracture zones inside the basement (Ma-lay Basin). Green areas indicate a higher frac-ture density.

Figure 10. Gradient magnitude of relative AIsections (violet to light blue areas are goodfracture zones) along well profile B-2 (A)and A-2 (B) (Malay Basin). Well B2 has not pro-duced hydrocarbons from the basement, butwell A-2 has produced oil from different zonesinside the basement.

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Figure 12. Dip-azimuth attribute maps of P-impedance at different levels below the car-bonate basement top. Example from the SongHong Basin.

Figure 13. Time slices showing the potentialof different seismic attributes for predictingparameters of different fracture systems, suchas dip, azimuth, continuity, and intersection.Example from the Malay Basin.

Figure 11. Seismic dip attribute maps at dif-ferent levels below the top of basement, and ahistogram of the strike of large solution-enhanced fractures for all wells in oil field Ain the Cuu Long Basin.

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confirm that a good fracture network is penetrated bythe wells, but unfortunately the basement test of well A-1 is not conclusive and the basement test for well B-1flowed water. Figure 14 shows an ant-tracking timeslice and results of FMI data interpretation for the meta-sedimentary basement in Malay Basin. This figureshows that fractures in different wells can have differ-ent azimuths, but the fracture azimuths are fairly con-sistent with the azimuths determined from seismicattributes at the well locations. Figure 15 presentsthe ant-tracking basement section along the A-2 wellprofile in the Malay Basin with DTCO, DTSM, andVP/VS logging curves showing good fracture zones inside

the metasedimentary basement. The points in thesection indicate the top of good fracture zones inter-preted by integrated FMI, sonic scanner, and mud log-ging data. There is good consistency between the top ofthe fracture zones with the seismic attribute.

As noted above, the quality of fractured basementreservoirs is determined by a combination of six proper-ties, but a single seismic attribute can be effective inpredicting only one or two properties. Therefore, theintegration of different seismic attributes is very impor-tant. Attributes can be integrated by different methods,from one as simple as the code method described inNgoc et al. (2011) to more involved methods such

Figure 14. Ant-tracking of relative AI timeslice 2280 ms inside the metasedimentarybasement of oil field A, with fracture/fault di-rections interpreted by FMI data. Examplefrom the Malay Basin.

Figure 15. Ant-tracking of relative AI sectionalong basement well A-2 profile comparedwith well logging data and the tops of the frac-ture zones interpreted by integrating FMI,sonic scanner, and mud logging data.Example from the Malay Basin.

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as volume blending of seismic attributes (Figure 16), orusing an artificial neural net method as discussed inAnh et al. (2009). In Figure 16, blending of reflectionintensity of gradient magnitude and ant-tracking of rel-ative AI attributes predicts the distribution of high frac-ture density and characteristics of different fracturesystems.

Conclusions and recommendationsSeismic attributes are effective tools for characteriz-

ing fractured basement reservoirs. They become espe-cially effective when integrated with other kinds ofgeologic and geophysical data.

For the zone closed to the basement top, the charac-teristics of fracture systems can be predicted by ampli-tude, coherence, curvature, and secondary derivativeattributes. For deep inside the basement, relative AIand its derived attributes successfully predict the distri-bution of high fracture density. Dip and azimuth, ant-tracking, and gradient magnitude attributes haveproven effective in predicting fracture characteristics.

The quality of the seismic data is paramount fordelineating and characterizing fractured basement res-ervoirs. We emphasize that, for basement reservoir ex-ploration, the input seismic data should be acquired andprocessed optimally, employing advanced solutions fordepth imaging. This is even more critical in basementexploration than it is in more conventional explorationsettings.

In future studies of fractured basement reservoirs,quantitative seismic attribute interpretation should bestrongly considered.

AcknowledgmentsWe express our sincere thanks to PETRONAS Cari-

gali Sdn Bhd, PVEP, and PVN for permission to

publish this paper. We also would like to thankCGGVeritas, WesternGeco, Fugro-Jason, DownUnderGeoSolutions, and Schlumberger for helping us carryout some of the studies.

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Ngoc, N. H., N. Q. Quan, H. N. Dong, and N. D. Ngoc Nhi,2011, Role of 3D seismic data in prediction of high po-tential areas within Pre-Tertiary fractured granite base-ment reservoir in Cuu Long Basin, Vietnam offshore:Search and Discovery Article #40702: AAPG.

Nguyen, H. N., Q. Q. Nguyen, N. D. Hoang, H. L. Pham, andN. H. Tran, 2010, Application of from seismic interpre-tation to tectonic reconstruction methodology to studyPre-Tertiary fractured granite basement reservoir inCuu Long Basin, Southeast Vietnam offshore: Searchand Discovery Article #40507: AAPG.

Nielsen, L. H., A. Mathiesen, T. Bidstrup, O. V. Vejbek, P. T.Dien, and P. V. Tiem, 1999, Modelling of hydrocarbongeneration in the Cenozoic Song Hong Basin, Vietnam;a highly prospective basin: Journal of Asian Earth Sci-ences, 17, 269–294, doi: 10.1016/S0743-9547(98)00063-4.

Sircar, A., 2004, Hydrocarbon production from fracturedbasement formations: Current Science, 87, no 2, 147–151.

Trinh, V. L., 2008, Pre-Cenozoic geological structure ofcoastal region of Dalat zone and its relationship withCuu Long basin: Presented at the 2nd InternationalConference on Fractured Basement Reservoir.

Nguyen Huy Ngoc received an M.S.(1980) and Ph.D. (1983) in prospec-ting geophysics from the Moscow Pro-specting Geological University inRussia. He joined PETRONAS CarigaliShd. Bhd. in 2011 as a senior reservoirgeophysicist. He has more than 20years of work experience in the geo-physical and petroleum industries

and more than 10 years of lecture experience in univer-sities. Before joining PCSB, he worked as a senior geo-physicist for Thang Long JOC, expert/senior seismicinterpreter for PETRONAS Carilgali Vietnam LimitedShd. Bhd, deputy exploration manager of Hoang Long

Figure 16. Blending (by weighted sum) time slice of relativeAI reflection intensity of gradient magnitude attribute and ant-tracking attributes clearly images the distribution of zones ofhigh fracture density as well as the character of different frac-ture systems inside the basement. Example from the MalayBasin.

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JOC, senior geophysicist with Petronas Carigali VietnamShd. Bhd., and as a lecturer of geophysics at the PetroleumFaculty of Hanoi University of Mining & Geology.

Sahalan Abd Aziz received a B.S.(1982) in geology from the NationalUniversity of Malaysia, and joinedPETRONAS in 1982 as a junior geolo-gist. He has more than 25 years work-ing experience as an exploration,development, and production geolo-gist with PETRONAS, covering Malay-sian basins in the Pakistani and

Indonesian area. At present, he is a senior manager atPetroleum Engineering Division, PETRONAS handling re-source maturation projects within development and pro-duction area.

Nguyen Anh Duc received an M.S.(2004) in geophysics from Ha Noi Uni-versity of Mining and Geology, Viet-nam, and became a deputy generalmanager of the exploration divisionof PVEP in 2006. He has more than18 years of work experience in thegeophysical and petroleum industry.Over five years, he has worked on

projects to estimate the reserves of Dai Hung field, Te GiacTrang field, Gau Chua-Gau Ngua field from 2003 to 2008;estimate fracture systems and calculation parameter inBasement granitoid of CNV field, RangDong field, GauChua field, and Hai Su Den field; performed petrophysicalanalysis on 24 wells in Venezuela, and 11 wells in blocks 46from 2007 to 2009. Currently, he is a postgraduate workingon a thesis titled “The application of seismic attributes andwell data for reservoir characterization in Pre-Tertiaryfractured basement in Cuu Long Basin.”

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