comparison of petrophysical rock types from core and well-logs … · seismic data. in order to...

Comparison of Petrophysical Rock Types from Core and Well-logs using Post-stack 3D Seismic Data: Field Example from Maracaibo-Venezuela Francisco Cheng* and Kumar Ramachandran, The University of Tulsa, Oklahoma, David Contreras, University of Texas at Austin. Summary A well log based reservoir study together with post stack 3D seismic data analysis was used to assess the petrophysical rock type distribution in an area located on the West side of Lake Maracaibo-Venezuela. The calculated petrophysical rock types were obtained using the Windland R35 (Gunter, et al, 1997) equation which includes information collected from core data: pore throat size distribution, porosity, and permeability. Permeability and rock type curves at the non-cored wells were predicted using available core data. Several consistency checks and quality control revisions were applied to obtain the results from these predictions in order to have a reliable relationship between petrophysical properties and petrophysical rock types. The resulting curves at the depth interval of interest were correlated throughout the field and calibrated with 3D seismic data. In order to obtain the correct parameter to enhance spatial rock type distribution, seismic attribute analysis was performed and a well log vs. seismic attribute cross-plot relationship was established to predict petrophysical rock properties in the interval. The predicted petrophysical rock type map provides extremely valuable constraints for the development of the field. The results clearly indicate that the application of this workflow is valuable for determining new locations to be drilled either for production or injection. Introduction The prediction of spatial distribution of petrophysical rock types within a heterogeneous siliciclastic reservoir is affected by significant uncertainties when based only on well and core information. However, integrating additional constraints, such as 3D seismic attributes, can significantly improve the accuracy of the reservoir model. In the process of modeling petrophysical properties, such integration is a key step to reducing uncertainties on static properties of a reservoir. The objective of this study is to determine a petrophysical rock type model by integrating core data, well logs, and seismic data. To achieve this objective, seismic attributes are used to improve petrophysical rock type model generated by the Winland equation (Gunter et al., 1997).

Mapping petrophysical rock types helps to asses the physical property distribution in highly heterogeneous reservoirs. The classification of petrophysical rock types may prove to be difficult given the non-linear relationship between total porosity and permeability in sandy rocks. Field Description The study area is located on the West side of Lake Maracaibo-Venezuela, in shallow, transition zone water depths. The field was discovered in 1955, but full-scale development did not commence until the 1970’s, pending the market for heavy oil. Since that time the field has produced heavy oil from the Oligocene Icotea and Eocene Misoa Formations. (Figure. 1)

Figure 1: Location of the study area. The predominant structure in the study area is a faulted anticline striking NE-SW, which occupies the central and northern part of the field. This anticline trend continues towards the south as a monocline with gentle dip. The eastern and southern limits are not well defined and the lake defines the limit to the west. The Icotea Formation (Oligocene Age) is comprised of continuous and homogenous sandstones. Particularly towards the southern part of the reservoir, where it is much thicker, it reaches up to 400 feet, representing the major producing interval of the field. Towards the north of the field it starts to become thin with a thickness of approximately 50 feet, with interlayers of sandstones and shales. The Misoa Formation is constituted by shales and sands, with predominance of sands in general and strong lateral variation of facies and high interstratification. Genetically it corresponds with environments of distributaries channel, distributaries mouth bar, crevasse splay and other sedimentary bodies of a fluvial-deltaic environment.

1595SEG Las Vegas 2008 Annual Meeting

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Comparison of rock types using seismic and well logs

Cut Offs R35 R35PRT min max

1 452 25 44.993 16 24.994 10 15.995 5 9.996 1 4.997 0.99

Methodology First, a petrophysical analysis was conducted with data from eight wells and one core to generate logs of porosity, permeability and water saturation for Misoa formation. Subsequently, a detailed fluid-substitution sensitivity analysis was performed in an effort to quantify the effect of fluid and lithology changes on elastic properties. Next, pore-throat radius test was conducted on core plugs and pore-throat aperture estimated from log data using Winland R35 (Gunter et al., 1997) in order to obtain petrophysical rock types. Finally, attribute analysis was applied to generate high resolution spatial distribution of original amplitude that estimates a best relationship between the petrophysical data and rock types. Petrophysical analysis Petrophysical evaluation techniques were used to construct logs of volume of shale (Vsh), water saturation (Sw), porosity and permeability. This procedure comprised the following steps:

1) Generation of Vsh from gamma ray logs using linear shale index.

2) Density porosity model. 3) Vsh correction of density logs. 4) Water saturation (Sw) and fluid corrected

effective porosity using dual water model. 5) Generation of logs of irreducible water saturation

Swirr. 6) Permeability prediction from total porosity,

volume of shale and water saturation using multilinear regression model.

Fluid substitution The Biot-Gassmann equation (Gassmann, 1951) allows us to determine the effects of pore fluids on wave propagation speed. This analysis substitutes known fluid, such as gas, oil and water, for the original pore contents in the formation, and make the wave speeds in the formation with the substituted fluids. The results from this exercise indicate that P-wave velocity and P-impedance decrease with an increase of hydrocarbon saturation. This behavior is corroborated by the petrophysical analysis of log and core data. Petrophysical Rock Types characterization Petrophysical rock types are units of rock deposited under similar conditions which experienced similar diagenetic processes resulting in a unique porosity-permeability relationship using Winland R35 equation.

The petrophysical rock types are generated through isolines determined through the Winland R35 equation: (Figure 2). LogR35 = 0.732 + 0.588 * log (Perm) – 0.864 * log (Phit) where Perm is permeability and Phit is total porosity.

Figure 2: Winland Porosity-Permeability plot. The Crossplot shows the range of reservoir quality associated with the observed rock types. Winland equation was used to estimate pore throat radius in noncored wells. R35 curves were generated for each well within the area. Based on this petrophysical features, seven petrophysical rock types have been established. (Table 1). Each rock types are characterized by a distinctive set of reservoir properties. The quality of each rock type is best for PRT 1 and deteriorates towards the PRT 7 (Figure 3).

Table 1: Classification of petrophysical rock types based on their R35 values.


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Figure 3: Petrophysical Rock Type characterization for Misoa formation in Well-A, showing the high vertical variation Seismic – Well Calibration The processes of calibration were accomplished by the creation of synthetic seismogram and matching of the geological marker with the correct seismic reflector (Figure 4).

Figure 4: Computation of the synthetic seismogram at the Well-A in the study area. The other method of calibration employed was to calculate time-depth curves with which the depths are led to double time of the seismic. Eight wells were calibrated using time-depth curves and correlated with the stratigraphic tops in the area. Seismic Attribute Analysis The 3D seismic data provide information to characterize lateral variations of physical rock properties, such as

porosity and permeability within a reservoir. The process of generating petrophysical rock type maps from seismic data involved several steps a) Interpret key horizons. b) Extract seismic attributes from the intervals of interest c) Identify relationships among the seismic attributes and reservoir properties from well data. d) Generation of rock types map between the actual values at the well locations and the integration of seismic attribute. Numerous seismic attributes were compared to rock type data using multiple crossplots to identify meaningful relationships between attributes and petrophysical rock types determined for Misoa formation. Original amplitude attribute provided reasonable correlations and was used in the subsequent analysis. Cross-Validation of the data Crossplots and statistical measures were used to quantitatively compare seismic attributes to petrophysical rock types at each well location. Correlation coefficient of 0.6 was obtained between seismic attribute and petrophysical rock types for the time windows. The negative correlation indicates that rock type is inversely correlated with amplitude data. (Figure 5).

Figure 5: Crossplot of the values of rock types versus original amplitude showing a good correlation coefficient for Well-A. the red points correspond to plot of petrophysical rock type and original amplitude. The blue line indicates the correlation. Integration of Seismic Attribute and PRT From Seismic data, each vertical column of cell in a 3D model yields a seismic attribute value. Co-kriging method was used to convert seismic attribute map to 3D petrophysical rock types map. The map of petrophysical rock types shows the tendency of high petrophysical rock type values to correspond to low seismic amplitude, and vice versa. This map was generated for a larger area that takes into account the data from all wells (Figure 6).


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Figure 6: Petrophysical Rock Type distribution estimated for the study zone with the help of 3D property modeling through seismic attributes. The purple and blue colors indicate the good quality in terms of porosity and permeability. Conclusions In this work, an integrated approach was presented for introducing petrophysical and seismic constraints in reservoir modeling. The main characteristics of this approach are: - The rock type characterization of the whole zone within the reservoir resulted in seven different petrophysical rock types. - The integrated interpretation of well logs, core data and surface seismic data combines the complementary strengths of two independent measurements. The combination of these measurements creates significant synergy and provides a new dimension in petrophysical characterization. - This methodology can be used in other fields where seismic data, petrophysical data from core and well logs are available. In addition, seismic data will provide information about interwell heterogeneity that is not available when using only well data.

Acknowledgements The authors thank the authorities of PDVSA for authorizing the publication of this work and NeXT-Schlumberger for supporting this research.


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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 2008 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES Bahar, A., A. O., Ghani, and M. Kelkar, 2004, Seismic integration for better modeling of rock type-based reservoir

characterization: A field case example: SPE 88793. Doyen, P. M., 1988, Porosity from seismic data: A geostatistical approach: Geophysics, 53, 1263. Gassmann, F., 1951, Elastic waves through a packing of spheres: Geophysics, 16, 673–685. Gunter, G. W., J. J. Pich, J. M. Finneran, D. J. Hartmann, and J. D. Miller, 1997, Early determination of reservoir flow units

using an integrated petrophysical method: SPE 38679. Han, D., and M. L. Batzle, 2004, Gassmann’s equation and fluid-saturation effects on seismic velocities: Geophysics, 69, 398–

405. Robinson, G., 2000, Incorporating seismic attributes with log data in reservoir modeling characterization: Presented at the 8th

International Conference and Exhibition. Silva, F., A. Ghani, A. Al-Mansoori, and A. Bahar, 2002, Rock type constrained 3D reservoir characterization and modeling:

SPE 78504. Xu, W., et al., Integrating seismic data in reservoir modeling: The collocated cokriging alternative: SPE 24742.


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