Rock physics based seismic reservoir prediction in the presence of thin coal beds Alexey Shubin*1, Dmitriy Klyazhnikov2, Yuriy Varov1, Valeriy Ryzhkov1

1Gubkin Russian State University of Oil and Gas, 2Ingenix Group, Moscow, Russia Summary We performed deterministic reservoir prediction based on inverse rock physics modeling for one oilfield in the West Siberia, Russia. The target zone is Tyumen formation. It consists of a thinly laminated sand-shale-coal sequences. Coal layers have very low acoustic impedance and affect seismic response. The basic idea of the proposed method is to calibrate the rock physics model and then apply it in reverse direction for reservoir properties evaluation. As a result, we transformed seismic scale elastic attributes P-impedance and Vp/Vs ratio into reservoir parameters: volume of clay, volume of coal and porosity. Introduction Quantitative seismic reservoir characterization remains a challenging problem for oil and gas industry. One of the most important task in this area is to transform seismically derived elastic attributes into reservoir parameters. Several techniques exist, among them linear and multiple regression, geostatistical approach, neural networks (Hampson et al., 2001), classifications, including Bayesian approach (e.g. Avseth et al., 2005) and inverse rock physics methods (Johansen et al., 2013). The idea of inverse rock physics is to apply calibrated rock physics model in reverse direction. In this case, the input data are elastic attributes, the output will be reservoir properties. However, the number of reservoir parameters is often higher than the number of seismic elastic attributes and this is known to be an underdetermined problem. But the problem can be simplified if we reduce number of reservoir properties and choose only key parameters for prediction. Beyond that, geological conditions can be applied, for example relationships between reservoir parameters. In this work we performed deterministic reservoir prediction based on reverse rock physics modeling. We used Raymer-Gardner-Hunt model (Raymer et al., 1980) in combination with Greenberg-Castanga (1992) method. In our case the rock physics models have two advantages: match real log data, require less parameters in comparison with advanced rock physics models and therefore simple for inversion. The technique was applied to a laminated sand-shale sequence with the presence of thin coal beds. Coal layers have very low acoustic impedance and affect seismic response. It may lead to problems with prediction using traditional methods, for example linear regression. As a result of inverse rock physics modeling, we transformed

seismic scale elastic attributes P-impedance and Vp/Vs ratio into reservoir parameters: volume of clay, volume of coal and porosity. The work was inspired by the research done by Spikes and Dvorkin (2005) who proposed to invert P-impedance into volume of clay and total porosity, considering petrophysical relation between volume of clay and porosity. We used this approach to include volume of coal in the inversion scheme. Data sets The proposed techique was applied to a real data set from a West Siberian oil field. Well data set included volume fractions of quartz, clay, calcite and coal, porosity, P-wave and density logs. Measured S-wave velocities were not available in all wells. Quantitative log interpretation of Tyumen shaly formations used a deterministic approach to generate volumetrics and lithology logs. Seismic data included commonly used elastic attributes P-impedance and Vp/Vs ratio. Figure 1 shows log data set from one well of the study area.

The target zone is Tyumen formation (Jurassic age). It consists of a thinly laminated sand-shale-coal sequences. The depositional environment is shore-continental from fluvial to lacustrine. The reservoir rocks are consolidated low to medium porosity sandstones. Porosity values range from 9% to 18%, the average is 14%. Jurassic sandstones are low-permeable, core permeability values do not exceed

Figure 1: Well log data set used for our study: lithology (yellow -sandstone (reservoir rock), green - shaly rock, brown - tight sandstone, black - coal), volume of clay, effective porosity, P-wave, S-wave, density. Measured - gray, modeled - red

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Rock physics based seismic reservoir prediction

10 - 15 mD, the average value is about 1.5 mD. Reservoir thicknesses vary from 0.5 m to 10 m. Rock physics model calibration At this step we chose a rock physics model that quantitatively explains measured log data and performed forward rock physics modeling. Selection of rock physics model depends on rock type and objectives of the reservoir characterization project. For unconsolidated sands contact models are common choice. Consolidated rocks can be modeled using inclusion models,

where we may specify shape and orientation of pores. Such models are relevant for carbonates and shales as well. Contact and inclusion models can be combined with Gassmann equation to calculate elastic properties of fluid saturated rock. The application of advanced rock physics models allows to include in analysis various rock properties: cement, sorting, pressure, pore types, oriented fractures etc. In some cases these parameters can significantly affect elastic properties and seismic response thus we have to take them into account (Avseth et al., 2009). On the other hand, we have to keep in mind the goals of the reservoir characterization project: lithology and porosity prediction. This implies that we need to find balance between rock physics model complexity and reservoir project objectives, in other words to apply Occam’s razor.

Effective elastic properties of the target interval directly depend on lithology, especially presence of coal beds, and porosity. Pore fluid influence is negligibly small for consolidated low to medium porosity sandstones. Once a rock physics analysis was performed with several models we found out that simple Raymer-Hunt-Gardner (RHG) model accurately fits the log data. Note that even elastic properties of a low porosity tight sandstone are very close to measured data (Figure 1). Figure 2 shows histogram of relative error between modeled and measured P-wave velocity. Applied RHG model can be described in the following equation:

1 where – P-wave velocity in the rock, – P-wave velocity in rock matrix, – P-wave velocity in fluid phase. is calculated from elastic moduli and densities of the constituent minerals using the Voigt-Reuss-Hill average. Shear wave velocity was predicted using Greenberg-Castanga (GC) method. Minerals and pore fluids properties are shown in Table 1. In our case RHG-GC model has two advantages: fits real log data, requires less parameters in comparison with advanced rock physics models and therefore simple for inversion.

Table 1: Elastic properties of minerals and fluids

Minerals/Fluids K, GPa G, GPa Density, g/cc Quartz 37 44 2.7 Clay 21 6.7 2.6 Coal 6 2.3 1.4 Brine 2.5 - 1

Method of inverse rock physics modeling Presence of two elastic attributes and calibrated RHG-GC model enable to solve inverse rock physics problem with respect to two reservoir properties: volume of clay ( ) and porosity ( ).

, , , , , , ,

where – P-impedance, – P-wave and S-wave velocities

ratio, , , – elastic moduli and density of mineral matrix, , – bulk modulus and density of fluid phase.

Figure 2: Histogram of relative error between modeled andmeasured P-wave velocity

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Rock physics based seismic reservoir prediction

If we want to add an additional variable, e.g. volume of coal ( ), then we need to append one more equation. Such an

equation could be a link between porosity and volume of clay (Spikes and Dvorkin, 2005). A linear relationship between two parameters is often observed for laminated sands and shales (e.g. Dvorkin at al., 2014). Figure 3 shows relationship between reservoir parameters for Tyumen formation.

, , , , , , , ,

∙

We used numerical solution for a set of equations to calculate reservoir properties curves. Results We applied inverse rock physics method based on RHG-GC model to upscaled elastic log curves and inverted seismic attributes.

Figure 4 shows porosity and volume of clay prediction for the section with no coal beds. Predicted curves (red) are very close to averaged log reservoir curves (black). Then we used the same method for a data set with thin coal beds and revealed a serious mis-prediction result for both reservoir parameters (Figure 5, blue curves), due to coal beds presence. At depth around 3100 m we may interpret wrong reservoir interval that is actually a shaly rock. This led us to include volume of coal into prediction scheme. Figure 5 shows the resulted curves (red) in case of considering volume of coal. Now inverted porosity and volume of clay curves are reasonably close to averaged log curves.

At final step we applied this method for inverted seismic data. Figure 6 shows volume of clay vertical section and a map of this parameter for the target interval. We didn’t use exploitation wells A and B for seismic inversion and rock physics analysis. Well A encountered 16.6 m of reservoir sandstone. Horizontal Well B penetrated into 218 m of reservoir rock.

Vclay

0 0.1 0.2 0.3 0.4 0.5 0.60

0.05

0.1

0.15

0.2

0.25

Coals

Figure 3: Crossplot of porosity versus volume of clay. Gray-log scale data, red – seismic scale data

Figure 4: Well log data set used for our study: P-impedance, Vp/Vsratio, volume of clay, effective porosity, lithology (yellow-sandstone (reservoir), green-shaly rock (non-reservoir). Log data –gray curves, inverted – red curves, upscaled – black curves

De

pth

, m

Figure 5: Well log data set used for our study: P-impedance, Vp/Vs ratio, volume of clay, volume of coal, effective porosity, lithology (yellow-sandstone (reservoir rock), green-shaly rock, brown-tightsandstone, black-coal). Log data – gray curves, inverted – red and bluecurves, upscaled – black curves.

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Rock physics based seismic reservoir prediction

Conclusion We found out that RHG – GC model fits measured elastic properties of Tyumen formation and can be applied for rock physics inversion and reservoir property prediction for West Siberian oil fields. Due to absence of measured S-wave velocities we aren’t completely confident about modeled Vs. In the future we plan to verify various S-wave predictors for Tyumen formation. As a result, here are some important considerations about applied reservoir characterization technique. First of all, if we want to evaluate deterministically more than two reservoir properties from seismic attributes Ip and Vp/Vs ratio, we have to append additional equations that link reservoir parameters and agrees with geological conditions. Presence of coal beds affects volume of clay and porosity estimation, therefore it is crucial to include coals into prediction. The rock physics model is important, it is necessary to choose the model that satisfies depositional environment and has a high prognostic potential. For this purpose we recommend to use one group of wells for calibration and another group of blind wells for verification. We observe that the rock physics model is scale independent and can be used for seismic reservoir prediction. Acknowledgments We gratefully acknowledge CGG, Schlumberger and Ikon Science for support Exploration Geophysics Department of Gubkin University.

Figure 6: Top: Volume of clay vertical section through well A, log data – volume of clay and lithology (yellow-sandstone, green-shaly rocks, black-coal). Bottom: Map of average volume of clay in the target interval. Reservoir sandstone marked yellow at well location

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EDITED REFERENCES

Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2017

SEG Technical Program Expanded Abstracts have been copyedited 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

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