PROCEEDINGS, Thirty-First Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California, January 30-February 1, 2006 SGP-TR-179

SEISMIC ATTRIBUTES IN GEOTHERMAL FIELDS

Bruno Kaelin, Moritz M. Fliedner and Dimitri Bevc

3DGeo Development Inc. 4633 Old Ironsides Drive, Suite 401

Santa Clara, CA, 95054, U.S.A. E-mail: bruno@3dgeo.com

Francis C. Monastero

US Navy Geothermal Program Office

429 E Bowen Road, MS 4011 China Lake, CA, 93555, U.S.A.

E-mail: francis.monastero@navy.mil

ABSTRACT

Large velocity contrasts are regularly encountered in geothermal fields due to poorly consolidated and hydro-thermally altered rocks. The appropriate processing of seismic data is therefore crucial to delineate the geological structure. To assess the benefits of surface seismic surveys in such settings, we applied different migration procedures to image a synthetic reservoir model and seismic data from the Coso Geothermal Field. We have shown that the two-dimensional migration of synthetic seismic data from a typical reservoir model resolves the geological structure very well despite the extremely strong and sharp velocity contrasts. In addition, small fracture zones can be inferred from the inspection of the common image gathers. After carefully preprocessing seismic data the 2-D and 2.5-D pre-stack depth migration of line 109 in the Coso Geothermal Field shows a well defined reflector at about 16’000 ft depth. Compared to the 2-D pre-stack migrated image the 2.5-D pre-stack migrated image resolves the deep reflector better, which indicates that the subsurface shows significant three-dimensional structures. The 3-D pre-stack depth migration at the intersection of line 109 and line 110 shows that the deep reflector lies horizontally and recedes in the cross-line direction of line 109. The results demonstrate that three-dimensional surveys greatly improve the image of the subsurface, where geological structures and seismic velocities vary both vertically and laterally.

From a variety of seismic attributes the offset stacks, the energy and the frequency prove to be the most powerful in the Coso Geothermal Field. The offset stacks show that the deep reflections are strongest and most continuous for 15’000 ft offset to 20’000 ft offset. The far-offset stack combined with the energy yields an almost perfect indication for the deep reflector. The frequency power spectrum shows distinct peaks, which may be related to the reflector thickness.

INTRODUTION

Over the past decade seismic imaging methods in hydrocarbon exploration have been greatly improved. Pre-stack depth migration has become the preferred tool to image complex geological structures. At the same time three-dimensional seismic surveys have proven to be indispensable to image salt bodies, dipping sedimentary layers and other complex geological structures. These newly developed imaging techniques are mainly applied to hydrocarbon exploration geophysics, though. In geothermal fields we face similar challenges, which make them an ideal candidate for the application of three-dimensional pre-stack migration methods. In geothermal fields large velocity contrasts and complex geological structures are regularly encountered. In many geothermal fields the subsurface consist of rocks, which have been hydro-thermally altered and exhibit abnormally strong vertical and lateral velocity variation. In addition, geothermal activity leads to many cracks and fractures, which make seismic surveys even more challenging due to strong scattering effects. Bevc et al. (2002) demonstrated the feasibility of improved seismic image resolution in geothermal fields through the application of state-of-the-art

seismic data processing techniques. They processed three representative two-dimensional seismic lines from a dataset acquired in the Coso Geothermal Field. In this paper we investigate three different aspects of seismic imaging methods in geothermal fields:

1) We investigate the feasibility to image geothermal reservoirs with a two-dimensional synthetic dataset from a typical geothermal reservoir model.

2) We present the processing results from two seismic lines from the Coso Geothermal Field after 2-D, 2.5-D and 3-D processing, respectively.

3) We compute seismic attributes from the seismic line 109 from the Coso Geothermal Field.

SYNTHETIC RESERVOIR MODEL

We computed the synthetic acoustic data with a finite difference code based on a typical reservoir model (Figure 1). The synthetic datasets consists of 67 shots every 30 m, starting at 1000 m in-line coordinate. The number and position of the geophones was kept constant, starting from 500 m in-line coordinate with 10 m spacing. Each of the 100 traces per shot consists of 1600 time samples with 1 ms sampling time.

Figure 1. Velocity model of a typical geothermal reservoir. There is a fractured zone with very low velocity in the center of the model at about 1000 m depth.

The zone of interest consists of the slow velocity fractured zone in the center of the velocity model at about 1000 m depth. To investigate the feasibility of imaging the small fractured zone as well as the complex structure of the layers, we computed the synthetics with the fractured zone and without the fractured zone, respectively. Figure 2 shows that the differences are significant, but they are hardly noticeable in the shot gathers only.

Figure 2. Synthetic shot gathers in the center of the velocity model. Figure a) shows the shot gather without the fractured zone, Figure b) shows the same gather with the fractured zone and Figure c) shows the difference between the two gathers, respectively.

Figure 3. Pre-stack wave-equation migration results from the synthetic dataset. Stacked migration image (left) and common image gather (right). The top of the fractured zone is visible as the diffraction at the corresponding depth in the common image.

Since we are purely interested in the structures below 500 m we restrict the seismic processing from this depth downward. For imaging we applied the pre-stack wave-equation migration without any additional processing. The image in Figure 3 shows the clearly defined interface between the two layers including all structural features. The image of the fractured zone is limited to the top of the zone itself. Due to the large velocity contrast between the layers most of the seismic energy is refracted and only little energy is reflected from the fractured zone back to the surface. However, pre-stack migration yields more information in the common image gather, which shows the diffraction from the top of the fractured zone. Hence, the fractured zone can be inferred from diffractions in the common image gather.

SEISMIC IMAGING OF THE COSO GEOTHERMAL FIELD

Preprocessing Detailed information about the seismic surveys in the Coso Geothermal Field can be found in Pullammanappallil et al. (2001) and Bevc. et al. (2002). Initial velocity models were generated based on the picking of first breaks on pre-stack data using seismic turning ray tracing and inverting the data to obtain parameterized velocity tomograms (Pereyra, 2000). The seismic data were preprocessed and statically shifted to a floating datum, applying static corrections and pre-stack energy enhancement (Figure 4). The deeper part of the velocity model was determined with Gamma-Scans and migration velocity analysis of the common image gathers.

Figure 4. Shot point 102 of Line 109 in the Coso Geothermal Field: Raw data (left) and preprocessed data (right) (from Bevc et al., 2002).

Pre-stack Depth Migration Based on previous studies line 109 in the Coso Geothermal Field shows a deep reflector at about 16’000 ft, which may be interpreted as the local brittle-ductile zone (Pullammanappallil et al., 2001; Unruh et al., 2001). Hence, we focused our investigation on this depth range only.

2-D and 2.5-D Pre-Stack Depth Migration We applied Kirchhoff pre- stack migration on the preprocessed data assuming two-dimensional geometry. The results show the deep reflector at about 16’000 ft depth extending laterally for about 12000 ft (Figure 5). Since the common mid points of line 109 show small variable cross-line components, we decided to process the data in 2.5-D with Kirchhoff pre-stack migration. Compared to the 2-D migration the stacked image resolves the deep reflector better, which indicates that the subsurface shows significant three-dimensional structures (Figure 6).

Figure 5. 2-D Kirchhoff pre-stack migration of line 109 in the Coso Geothermal Field. Clearly visible is the strong reflector at about 16’000 ft depth.

Figure 6. 2.5-D Kirchhoff pre-stack migration of line 109 in the Coso Geothermal Field. The main differences between the 2-D and the 2.5-D migration are indicated.

3-D Pre-Stack Depth Migration Line 109 crosses line 110 at 11000 ft in-line coordinate, which allows a 3-D migration for a small image cube of 4000 ft in-line length and 4500 ft cross-line length. However, the velocity model was assumed to remain constant, because the data fold was too small for a three-dimensional velocity analysis.

Figure 7. 3-D Kirchhoff pre-stack migration of a small image cube at the intersection of line 109 and line 110 in the Coso Geothermal Field. The three-dimensional structure of the deep reflector becomes clearly visible.

The 3-D migrated image in Figure 7 shows that the deep reflector extends horizontally and recedes in the cross-line direction. More reflectors, which could not be resolved by 2-D migration and 2.5-D migration, become visible.

SEISMIC ATTRIBUTES

Seismic attributes are inferred physical quantities from seismic data and are commonly used in hydrocarbon exploration and reservoir monitoring. The goal is to find attributes, which are characteristic for a particular area. Hence, we investigated several commonly used attributes for the reflector at 16’000 ft in the Coso Geothermal field to characterize this reflector.

Near-Offset Stack and Far-Offset Stack Near-offset stacks and far-offset stacks are preferred tools to characterize geological areas. We computed the near-offset stack with 10’000 ft maximum offset and the far-offset stack with 10’000 ft to 24’000 ft offset (Figure 8). In the near-offset stack the deep reflector is not visible at all, whereas the far-offset stack resolves it clearly. This may be due to the physical characteristics of the reflector, but it may also be the result of the seismic processing in this challenging environment.

Figure 8. Line 109 in the Coso Geothermal Field: Near-offset stack (left) and far-offset stack (right). The deep reflector is visible in the far-offset stack only.

Energy Figure 9 shows the energy at line 109 in the Coso Geothermal Field in the common image gather. The energy was computed between 15’000 ft and 18’000 ft by taking into account the lateral coherency of the reflections. Most of the energy is concentrated at far-offsets between 15’000 ft and 22’000 ft. Since almost all the reflected energy is located in the far-offset data, we computed the energy of the far-offset data only (Figure 10). The computed energy illuminates the deep reflector only, which makes the combination of far-offset data combined with the energy a perfect attribute to locate similar reflectors.

Figure 9. Energy as a function of offset in the common image gather. The far-offset data contains most of the reflected energy.

Figure 10. Line109 in the Coso Geothermal Field: Energy of the far-offset data with the overlain seismic image. The deep reflector is well illuminated.

Figure 11. Line 109 and line 110 in the Coso Geothermal Field: Energy attribute in a small 3-D cube at the intersection of the two seismic lines.

Energy in 3-D Seismic attributes are especially useful in three-dimensional surveys, because they allow a better visualization of geological structures. Figure 11 shows the energy attribute for a small image cube at the intersection of line 109 and line 110 in the Coso Geothermal Field. Compared to the initial migrated

image the energy attribute illuminates the three-dimensional character of the reflector and the heterogeneity of the subsurface much more clearly.

Frequency The frequency power spectrum was computed from 15’000 ft to 18’000 ft depth. The spectrum shows

distinct single frequency peaks in the in-line direction (Figure 12). Compared to the source frequency content from 4 Hz to 30 Hz, the frequency content of the reflector is smaller and lies between 4 Hz and 20 Hz.

Figure 12. Line 109 in the Coso Geothermal Field: Frequency power spectrum from 15’000 ft to 18’000 ft depth. The spectrum shows distinct single peaks in the in-line direction.

Reflectivity For further investigation of the characteristic peaks of the frequency power spectrum the power spectrum of the reflection response must be computed. To extract the reflectivity power spectrum we applied the procedure:

1) compute the frequency spectrum, 2) divide the frequency spectrum by the source

spectrum to obtain the Green’s function, 3) compute the attenuation, 4) compute the Quality factor Q, 5) compensate for the intrinsic attenuation

between surface and reflector. We obtained the source spectrum by stacking several direct waves close to the source. The Quality factor Q was computed with linear regression from 8 Hz to 20 Hz with the spectra from 10’000 ft in-line coordinate to 24’000 ft in-line coordinate. We found that the Quality factor Q = 55 fits the data best between the surface and the reflector at 16’000 ft. The reflectivity power spectrum shows similar features like the frequency power spectrum, but the frequency band-with is increased and the frequency peaks are slightly shifted to higher frequencies (Figure 13). These changes become even more apparent, when the spectra are stacked over the in-line direction (Figure 14).

Figure 13. Line 109 in the Coso Geothermal Field: Reflectivity power spectrum from 15’000 ft to 18’000 ft depth. The spectrum shows distinct single peaks in the in-line direction.

Figure 14. Line 109 in the Coso Geothermal Field:

Stacked power spectra along the in-line direction from 15’000 ft to 18’000 ft depth.

Single Layer Hypothesis After analyzing the attributes we can make the hypothesis that the deep reflection is caused by a horizontal geological structure, which causes distinct frequency peaks in the power spectrum. The simplest model for such a reflector is a single horizontal layer with thickness d. Such a layer produces reflection peaks for λ/d = 1/4, 3/4, 5/4, …, where λ is the seismic wavelength within the layer. After analyzing the reflectivity power spectrum in the in-line direction, we can conclude that there are single peaks only at each location. Hence, we can assume that the peaks represent the first maximum reflection and therefore the ratio of wavelength and layer thickness is one quarter at each location. Table 1 shows the resulting reflector thickness between 260 ft and 450 ft for the three dominant frequencies.

Table 1. Coso Geothermal Field: Reflector thickness at 16’000 ft depth for the three dominant

frequencies.

Frequency (Hz) 8 12 14 Wavelength (ft) 1800 1200 1035 Layer thickness (ft) 450 300 260 The maximum power of the reflection is determined by the impedance contrast between the layer and the surrounding rock. At 16’000 ft depth the density differences are generally small compared to the velocity differences. We therefore assume constant density and compute the maximum reflection coefficient for different velocities in Table 2. The results show that lower velocities cause much stronger reflections and can therefore more easily be detected.

Table 2. Coso Geothermal field: Maximum reflection coefficient for different velocities at

16’000 ft depth. Velocity (ft/s) 8000 14500 20000 Reflection coefficient -0.29 0.00 +0.15

CONCLUSIONS

We have shown that the two-dimensional migration of synthetic data from a typical reservoir model resolves the geological structure very well despite the extremely strong and sharp velocity contrasts. In addition, the small fracture zone can be inferred from the inspection of the common image gathers. After carefully preprocessing seismic data the 2-D pre-stack depth migration of line 109 in the Coso Geothermal Field shows a well defined reflector at about 16’000 ft depth. Since the common mid-points of line 109 show small variable cross-line components, we were able to process the data in 2.5-D with Kirchhoff pre-stack migration. Compared to the 2-D migrated image the 2.5-D migrated image resolves the deep reflector better, which indicates that the subsurface shows significant three-dimensional structures. The 3-D pre-stack depth migration at the intersection of line 109 and line 110 illuminates the three-dimensional structure of the reflector. The results demonstrate that three-dimensional surveys greatly improve the image of the subsurface, where geological structures and seismic velocities vary both vertically and laterally. From a variety of seismic attributes the offset stacks, the energy and the frequency prove to be the most powerful in the Coso Geothermal Field. The offset stacks show that the deep reflections are strongest and most continuous for 15’000 ft offset to 20’000 ft offset. The far-offset stack combined with the energy yields an almost perfect indication for the deep reflector. The frequency power spectrum shows

distinct peaks, which may be characteristic for the reflector thickness.

REFERENCES

Bevc, D., Fliedner, M.M. and Pereyra, V. (2002), "Increasing efficiency of geothermal energy generation with high resolution seismic imaging”, EISG Award Grant, Project 00-10, 33 pp. Pereyra, V., Ray tracing methods for inverse problems. Inverse Problems, 16:R1-R35 (2000). Pullammanappallil, S., Honjas, W., Unruh, J., and Monastero, F. (2001), "Use of advanced data processing techniques in the imaging of the Coso Geothermal Field”, 26th Workshop Geothermal Reservoir Engineering, Proc. 26, 8 pp. Unruh, J., Pullammanappallil, S., Honjas, W., and Monastero, F. (2001), "New seismic imaging of the Coso geothermal Field, Eastern California”, 26th Workshop Geothermal Reservoir Engineering, Proc. 26, 7 pp.

ACKNOWLEDGEMENTS

This work was supported by the California Energy Commission (CEC) through the EISG Award 04-01-05. We thank the US Navy Geothermal Program Office for providing the seismic data from the Coso Geothermal Field. We also thank John Queen and Ernest L. Majer from Lawrence Berkeley National Laboratory for providing the synthetic model of a geothermal reservoir. We would like to acknowledge the contributions of Victor Pereyra and Robert W. Ferguson in the preprocessing of the seismic data.