48 seismic facies analysis for fluvial depositions

8/13/2019 48 Seismic Facies Analysis for Fluvial Depositions

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EAGE 66th Conference & Exhibition — Paris, France, 7 - 10 June 2004

AbstractSeismic facies analyses have become an important part of present day exploration anddevelopment of oil and gas plays. A large part of the value of seismic facies analysisdepends however on whether or not local geological factors have been taken into account

correctly. The importance of this is even more dominant when dealing with lithological orcombined structural-lithological traps.

To illustrate such seismic facies analysis this paper presents the results of a study of thedeep oil and gas field in the Fergana Valley, a mega-syncline inside of the Tyan-Shan zone,Uzbekistan. The main productive formation in this field is consisting of large channel systemsand fluvial plain deposition facies. The channel systems are complicated and show rapidlateral and vertical variations of sandstone properties both in shale content as well as grainsize composition.

Single stack acoustic or elastic inversion alone could not discriminate between sandstoneand shale. Global Simultaneous AVO Inversion was found to be able to make thisdiscrimination. As a result of the study the geological model of the productive formation was

refined and a better understanding of the subsurface resulted in a better volumetrics andconnectivity estimation.

IntroductionGeologically, the study area is a faulted anticline where the main fault is reversed andseparates the structure into two parts: the Northern area and the Southern area. Theproductive Neogen deposition (a.k.a. KKC2 or red-brick formation) is composed ofcontinental sediments, mainly paleo river systems sandstones and fluvial plain shales.

In order to delineate the geometry and extent of the channel system, a seismic faciesanalysis based on seismic and well data was conducted. During this analysis lithologicalcomposition, effective porosity, hydrocarbon saturation and facial composition as well asproductive formation markers correlation and high-pressure zones have been estimated fromwell data penetrating the Neogen formation.

Method

As simple seismic interpretation on post-stack inversion is not able to discriminate betweenthe producing sandstones and the interbedded shales, the away-from-well information wasdeveloped using a proprietary global Simultaneous AVO Inversion technology. This inversiontechnology produces three base volumes: acoustic impedance (Zp), shear impedance (Zs),

and density (ρ). Next to these secondary combined volumes can be developed such as

Poissons ratio (PR), MuRho (µρ) and LambdaRho (λρ).

A012 SEISMIC FACIES ANALYSIS FOR FLUVIALDEPOSITIONS CHARACTERIZATION, THEFERGANA VALLEY EXAMPLE

T.L. BABADZHANOV1, R.LARIJANI

2, G. SHILOV

2, А. AHVERDIEV

2 AND А. RYKOV

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1OAO Uzbekgeophyzika, Tashkent

2 Fugro-Jason, 125009, Moscow,Tverskaya я , 16 / 2, building1



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An essential aspect to warrant the validity and minimize the uncertainty of the quantitativeinformation is a sequence of data conditioning, QC and processing steps.

Firstly the seismic 3D data needs to be reprocessed for amplitude preservation. This seismicdata is then cut into partial angle stacks of near (5-12), medium (9-18) and far angles (16-35). Secondly log editing, log calibration and petrophysics modeling needs to be performedbased on all wells. Vp, Vs and bulk density are then conditioned to rockphysics models in

every well of the project [1]. The log modeling results are used to predict lithofacies, reservoir

lithology and fluids, and to estimate wavelets for each partial angle stack. They also serve asa basis for the low frequency model used in the inversion. Finally the three partial anglestacks, the extracted wavelets, the constraints and the well log based low frequency modelsare used as input data to a global Simultaneous AVO Inversion.

In the illustrated workflow used in this study seismic facies delineation is based on correlation

relations between seismic inversion attributes (Zp, Zs, ρ, λρ, µρ, etc.) and facies reservoirproperties determined from well, log and core data. A common error is that the correlationsare done without differentiation into separate subsets of different lithofacies. During faciesdetermination based on log and core data it is necessary to take into account severalgeological in-situ factors [2].

For instance for terrigenous deposits the standard facial interpretation workflow of the log

data includes the following steps: log data quality control, lithofacies post-detrital changesestimation, lithological composition and reservoir properties determination, facial genesisanalysis with help of genetic models of the log facies, and finally paleotectonic analysis.

Log data can be used as well for over-pressured zone delineation. Knowledge of geothermalconditions and over-pressured zones correlating to productive formations help us determinediagenetic trends for rock physical properties.

Seismic facies analysis normally should be conducted after structural interpretation is

finished and all structural ambiguities are resolved. With the derived volumes of Zp, Zs, ρ, λρ

and µρ the extent and geometry of key stratigraphic formations and the main fault can bedetermined. This loop gives us a refined and geologically correct model of the reservoir.

The quantitative petrophysical interpretation of the Global Simultaneous AVO inversionresults were conducted according to the next workflow:

Reservoir properties evaluation

1. Well data analysis.

Evaluation of the best correlations between seismic inversion attributes (Zp, Zs, ρ,

λρ and µρ) and petrophysical and reservoir properties (porosity, Hsr, shaliness,Vsilt, Vclst, Vdol, Vlimst, etc.).

2. Averaged seismic inversion attributes analysis in the target intervals.

Evaluation of the best correlation relationship between averaged inversionattributes and averaged petrophysical and reservoir (elastic) properties.

Seismic Inversion attributes correlation

1. Seismic inversion attributes calibration based on the best-derived correlationrelationships. Mapping of average and RMS values of the seismic inversionattributes in the target intervals.

2. Correlation of the relationship between averaged seismic inversion attributes andaveraged reservoir properties (hydrocarbon saturated thickness, linear reserves,Net Pay, effective porosity).

Petrophysical and lithofacies modeling

1. Reservoir properties interpolation based on evaluated and validated correlation

relations between seismic inversion attributes and reservoir properties.



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EAGE 66th Conference & Exhibition — Paris, France, 7 - 10 June 2004

2. Seismic facies analysis based on seismic inversion attributes crossplots (Zp-Zs,

ρ- Zp).

In general the targeted Neogen interval can be delineated on the majority of the well data.

Particularly, effective porosity has a good correlation (more then 0.7) with Zp, with ρ and with

λρ. From the inverted seismic attributes calibration, averaged in the objective interval, it was

shown that the attribute λρ gives the best correlation to effective porosity (with correlationcoefficient of -0.8,) with relative error 0.017. It was found as well that there is a good

correlation between the lower density values and the higher oil saturated thickness intervals(with correlation coefficient -0.83).

As an example Figures 1 and 2 show two of these cross plots generated for the formation

Neogen. Figure 1 shows a crossplot of effective porosity at well control versus λρ from

seismic inversion. Figure 2 shows a well log crossplot of ρ versus Zp. The polygon in thisfigure may be used to separate the shaley fluvial plain facies from the channel facies.

Based on all the information and relationships as described above a sequence to transforminverted seismic attributes to reservoir properties was developed. The close relationshipsbetween petrophysical and reservoir properties on the one hand and seismic invertedattributes on the other hand, as well as lithology and facies-confined characterization of thereservoir made it possible to conduct seismic facies analysis and to map the different facies.

Figure 3 shows how the lithofacies modeling results in a map discriminating the fluvial planefacies from the channel facies.

CONCLUSIONIt was found that the targeted stratigraphic interval Neogen is composed of medium andcoarse-grained sandstones as well as siltstones that are genetically related to a main and asecondary paleo river channel system. Oil saturated intervals tend to coincide with low claycontent which in turn corresponds to the intervals of lower values in inverted seismicimpedance and lower density.

3D seismic interpretation results including facies analysis from seismic inversion combinedwith log and core data demonstrated that NEOGEN reservoir geobodies were geneticallyassociated with river channel complex sandstones and siltstones.

Oil pools associated with the above sandstones and siltstones are complicated in nature andstructurally, tectonically and lithologically distinct. Following from this reservoircharacterization process a refined geological model of the study area was built and reserveestimates were improved.

ACKNOWLEDGEMENTS

The authors wish to thank OAO UZBEKGEOPHYSIKA for the authorization to publish thispaper.

REFERENCES

[1] S. Xu and R.E. White. A new velocity model for clay-sand mixtures. GeophysicalProspecting, 1995, 43, pp 91-118.

[2] G.Y. Shilov and I.S. Dgapharov. Genetic models of sedimentary and vulcanogenicrocks and technology of facial interpretation based on geological and geophysical data,2001, Moscow.

[3] J. Fowler, M. Bogaards and G.Jenkins. Simultaneous inversion of the Ladybugprospect and derivation of a lithotype volume. SEG annual meeting, 2000,Calgary.



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Figure.1: Correlation relation between effective porosity from the wells and λρ from inversion averagedover the NEOGEN interval.

Figure 2: Cross plot of density versus seismic impedance for 9 wells for the NEOGEN formation, colorcoded by Vclay. The polygon may be used to discriminate fluvial plain facies (shale) from channelfacies (sand-siltstone).

Figure 3: NEOGEN formation channel facies (green) and fluvial plain facies (red) map based onlithofacial analysis.

48 seismic facies analysis for fluvial depositions

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