Dolomite Reservoir Delineation by Integrating Paleotopography and Seismic Attribute Analysis

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Dolomite reservoir delineation by integrating paleotopography and seismic attribute analysisYandong Li*, Wei Liu and Yan Zhang, Research Institute of Petroleum Exploration and Development,

PetroChina Co. Ltd.

Summary

Dolomite reservoir characterization poses great challengesto exploration geophysicists, mainly because of its complexgeologic origin and peculiar petrophysical properties. Theformation of good dolomite reservoir is closely related todissolution effect which is controlled by paleotopography.

Therefore, we implement dolomite reservoircharacterization at a study area in the Precaspian Basinusing an integrated seismic attribute analysis approach

constrained by paleotopography. The paleotopographyprovides background for determining the favorable faciesbelt with strong dissolution effect. Coherence and volumecurvature attributes are used to characterize faults and

fractures which determine the formation of porous dolomitereservoir. Finally, spectral decomposition is utilized to

highlight amplitude anomalies caused by dissolveddolomite reservoirs. Using this integrated approach, wesuccessfully delineated the distribution of the dolomitereservoir in the study area, and the results were calibratedand validated by the 36 drilled wells.

Introduction

Dolomite reservoirs are attractive exploration targets

throughout the world. However, its characterization isdifficult due to its complex geological origin and peculiar

petrophysical properties (Streckeret al., 2004). There arenumerous kinds of mechanism for dolomitization and to

determine which one is dominant in a given area is quitedifficult (Logel, 2004). Furthermore, the petrophysical

property of the dolomite reservoir is peculiar, with highvelocity, high modulus and small contrast betweenreservoir and non-reservoir rocks.

The formation of good dolomite reservoir is closely relatedto dissolution effect and the resulted karsting, which iscontrolled by the paleotopography. Therefore, we firstrecover the paleotopography of the dolomite formation to

recognize favorable facies belt with strong dissolutioneffect. Then we perform reservoir characterization usingseismic attribute analysis. Since dissolution is closelyrelated to the faults and fractures which determine theformation of good dolomite reservoir, we use coherence

and 3D curvature to delineate faults and fractures (Hart etal., 2009). Finally, spectral decomposition is combinedwith coherence and curvature to better delineate thedistribution of the dolomite reservoir. The reservoircharacterization results were calibrated with the 36 drilled

wells and the results verified the effectiveness of ourapproach.

Paleotopography Recovery

Paleotopography has a significant effect on the distributionof reservoir porosity and permeability (Dubois, 1980). To

recover the paleotopography of the target formation, areference horizon above the target formation is selected.The time thickness between the reference horizon and thetop of the target formation (isochron map) roughly reflectsthe paleotopography of target formation where dissolution

occurs. The selection of reference horizon above the targetformation is crucial for the recovery of the paleotopography.Generally, a maximum flooding surface under which shalesdeposit can be a good candidate. It is because that after the

deposition of the target formation and post-depositionalexposure, shales will deposit and quickly fill the

paleotopography lows. Thus the isochron map couldroughly reflect the paleotopography of the target formationif we neglect the compaction effect which is minor in our

study area.

Seismic Attribute Analysis

The paleotopography only provides information about thelikely locations where dissolved dolomite reservoir will

develop. The detailed distribution of the dolomite reservoirmust be obtained by integrating the seismic attributeinformation. Here we mainly use coherence, curvature and

spectral decomposition. Coherence helps to characterize

faults and fractures and curvature indirectly measures thedegree of development of fractures. These two attributesare closely related to the formation of dolomite reservoirand subsequent dissolution. In addition, spectral

decomposition attribute is used to highlight amplitudeanomalies caused by the porous dolomite reservoir.

Real Data Example

The real data example is from the Precaspian Basin whereCarboniferous carbonate reservoirs are developed under thePermian salt overhangs, which are obvious in the seismic

profile in Figure 1. The seven horizons from the top to thebottom are J: Bottom of Jurassic, Salt: Top of the salt dome,P1: Base of the salt dome, KT1: Top of Carboniferous, also

being the top of the first carbonate formation, MKT: Baseof the first carbonate formation, KT2: Top the secondcarbonate formation, VISEAN: Base of the secondcarbonate formation. The huge salt domes in this area posegreat challenges to seismic processing and the followingreservoir characterization. There are two target layers in

this area. The first is the Upper Carboniferous dolomite

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Dolomite reservoir delineation using seismic attribute analysis

reservoir and the second is the Middle Carboniferous

limestone reservoir. Our purpose is to delineate thedistribution of the dolomite reservoir in the first target layer

between KT1 and MKT.

The formation of porous dolomite reservoirs at thePrecaspian Basin is closely related to dissolution which hasa significant effect on reservoir porosity and permeability.This can be clearly demonstrated in the dolomite reservoircore photo in Figure 2 in which we can see well developed

dissolved pores and vugs. The dissolution of dolomitereservoir is closely linked to the paleotopography (Lucia,2007). This effect is most intense in the ramp area.Therefore, we recovered the paleotopography of the

Carboniferous dolomite formation using the methodmentioned above. The Lower Permian formation mainlyconsists of shale. Thus it will fill the paleotopographic lowsduring sedimentation processes. The top the LowerPermian formation is used as reference horizon to recover

the paleotopography. Therefore, by calculating the timethickness between P1 and KT1, we can roughly recover the

paleotopography of the top of the KT1 formation. Therecovered paleotopography is shown in Figure 3, with thegreen indicating the paleotopographic highs while themagenta the paleotopographic lows. The relief in Figure 3

shows nearly monotonic decrease from high to low and theflushed channels are well developed. The paleotopographichigh is unfavorable facies belt for the formation of

dissolved dolomite reservoir. The ramp is the most favoriteplaces where dissolved dolomite reservoir develops. Thisarea can be roughly delineated by the red and light blue.The magenta is the paleotopographic low which isunfavorable for the development of dissolved dolomite

reservoir.

There are a total of 36 wells in the study area. According tothe development of the dolomite reservoir in the targetformation, we divide these wells into two classes, Class Iwells with thick porous dolomite reservoir and Class II

wells with thin non-porous dolomite reservoir or with nodolomite reservoir at all. There are 19 Class I wells whichare marked by the filled black circles and 16 class II wells

which are indicated by filled white circles. For the 19 ClassI wells with thick porous dolomite reservoir, 17 wells arelocated in the favorite facies belt in the paleotopographic

background. For the 16 class II wells, only 3 wells locatedin the favorite facies belt and the other 13 wells are outside

the favorite facies belt.

To delineate the distribution faults and fractures, wecalculate average coherence in the target dolomite reservoirformation. The results are shown in Figure 4. The class Iwells are indicated by the filled red circles while the class

II wells are indicated by filled white circles. Figure 4 showsthat the Class I wells have a close relationship to faults and

fractures indicated by low coherence regions. Figure 5shows the most positive curvature attribute in the targetformation, which demonstrates that the class I wells isclosely related to curvature anomalies. We note that porous

dolomite reservoir often causes low amplitude anomalies.As an example, we show an arbitrary seismic profileintersecting one Class II well B and another Class I well Ain Figure 6 in which we see that the amplitude at well A is

significantly lower than that at well B. Therefore, weextract interval attribute of the target formation from thespectral decomposed 20 Hz iso-frequency volumes anddisplay the results in Figure 6. The results show that mostof the Class II wells are located in the high amplitude areas,

while most of the Class I wells are located in the lowamplitude areas. The discrepancy is due to the interferencesfrom shale adjacent to the dolomite reservoir which alsocauses amplitude anomalies.

Conclusions

We performed dolomite reservoir delineation using anintegrated paleotopography and seismic attribute analysis

approach. Under the constraint of paleotopography, we usecoherence, 3D curvature and spectral decomposition todelineate faults and fractures and amplitude anomaliesassociated with the dissolved porous dolomite reservoir.Calibrating the reservoir characterization results with the36 drilled wells shows that the proposed approaches

effectively delineate the distribution of the dolomitereservoir in the study area at the Precaspian Basin.

Acknowledgments

The work is co-supported by Key Project on OverseasHydrocarbon Exploration and Development, CNPC andChina State Key Science and Technology Project on

Marine Carbonate Reservoir Characterization(2008ZX05004-006).

Figure 2: Core photo of the porous dolomite reservoir at a study

area in the Precaspian Basin which clearly shows dissolved

touching vugs



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Figure 1: Seismic profile through 3D seismic data volume in which salt domes are obvious. The target dolomite reservoir

formation is between the KT1 and MKT. KT1 is an unconformity above which dissolution effect is strong

Figure 3: Paleotopography of top of Carboniferous formation.

Green indicates highest places and magenta the lowest ones. Thered and light blue are the ramp facies belt where dissolution effect

is strong. The filled black circles represent Class I wells with

thick porous dolomite reservoir while the filled white circlescorrespond to Class II wells with thin non-porous dolomite

rseseroivr

Figure 4: Average coherence in the target dolomite reservoir

formation. The 19 filled red circles represent Class I wells withthick porous dolomite reservoir while the 17 filled white circles

correspond to Class II wells with thin non-porous dolomite

rseseroivr. The Class I wells are closely related to faults and

fractures indicated by the low coherence regions

J

Salt

KT1

MKT

KT2

Visean

P1



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Figure 7: Interval attribute of the 20 Hz iso-frequency volume in

the target formation. Low amplitude areas indicate favorable

facies belt for porous dolomite reservoirs

Figure 6 Arbitrary seismic profile connecting well A with thick porous dolomite reservoir and well B with thin tight limestone reservoir.

The inserted well curves are porosity which exhibit conspicuous difference between well A and B

Figure 5: Average most positive curvature in the target formation

Most of the Class I wells are located in or near the regions with

curvature anomalies

B A



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EDITED REFERENCES

Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2010

SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata foreach paper will achieve a high degree of linking to cited sources that appear on the Web.

REFERENCES

Dubois, M. K., 1980, Paleotopography's influence on porosity distribution in the Lansing-Kansas City"E" zone, Hitchcock County, Nebraska: AAPG Bulletin, 64, 701.

Hart, B. S., J. A. Sagan, and O. C. Ogiesoba, 2009, Lessons learned from 3-D seismic attribute studies ofhydrothermal dolomite reservoirs: CSEG Recorder, 18-23.

Logel, J. D., 2004, Pitfalls in rock properties and lithology prediction in dolomite: SEG, ExpandedAbstracts, 260-263.

Lucia, F. J., 2007, Carbonate reservoir characterization-an integrated approach: Springer.

Strecker, U., M. Smith, R. Uaen, M. B. Carr, G. Taylor, and S. Knapp, 2004, Seismic attribute analysis inhydrothermal dolomite, Devonian Slave Point Formation, Northeast British Columbia, Canada: SEG,Expanded Abstracts, 378-381.

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Dolomite Reservoir Delineation by Integrating Paleotopography and Seismic Attribute Analysis

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