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5. AVO-AVA Introduction Weak-contrast, short spread approximation for reflection coefficient Information content Classification Tuning effect Examples.

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Presentation on theme: "5. AVO-AVA Introduction Weak-contrast, short spread approximation for reflection coefficient Information content Classification Tuning effect Examples."— Presentation transcript:

1 5. AVO-AVA Introduction Weak-contrast, short spread approximation for reflection coefficient Information content Classification Tuning effect Examples

2 Introduction AVO (amplitude versus offset) AVA (amplitude versus angle) The link between AVO and AVA is ray traycing in overburden.

3 Introduction Seismic lithology is the process by which rock properties such as lithology, porosity and pore fluid content are determined by analysis of seismic and other data. Ideally, seismic data would uniquely determine velocity, attenuation, and anisotropy for P-waves and S-waves as functions of angle. AVO has the advantage of being applicable to many conventional seismic data sets without the requirements of prohibitive acquisition, processing, and analysis costs.

4 Weak-contrast, short spread approximation for reflection coefficient P-wave: R(0) is intercept, G is gradient and K is curvature C-wave: B is intercept and C is curvature (5.1) (5.2)

5 Information content Anomalously low Vp/Vs ratios caused by hydrocarbons produce anomalous AVO response However, Vp/Vs ratios can not be uniquely inverted from AVO data alone

6 Vp versus Vs Castagna, 1993

7 Vp versus density Castagna, 1993

8 Vp/Vs versus Vp Figure 5.1. Vp/Vs ratio versus Vp for different lithology

9 Rss versus Rpp Figure 5.2. S to P reflection coefficients for different lithology

10 Gas-brine properties distributions Figure 5.3. Seismic properties for gas/brine sands

11 Some conclusions For large negative P-wave reflection coefficients, gas- sand and brine-sand reflection coefficients are distinct for all shale velocities. The lower the shale velocity, the greater the separation For small P-wave reflection coefficients, gas-sand and brine-sand reflection coefficients are well separated only for low shale velocities and only if the shale velocity is approximately known For large positive P-wave reflection coefficients, gas- sand and brine-sand reflection coefficients are well separated only for the lowest shale velocities and only if shale velocity is approximately known

12 AVO checklist Is the expected rock properties variation sufficient to produce a detectable AVO anomaly? Can the same seismic response result from other earth models? If AVO correctly predicts the occurence of hydrocarbons, what are the chances that the saturation will be commercial? Is there sufficient angular coverage for the event of interest? How do I know that processing has preserved and isolated the ”true” relative AVO response? What is the seismic data quality? Overburden? Processing? Does the AVO anomaly conform the structure? Do I understand what ”red” on the AVO display really means in physical terms

13 AVO misconceptions Myth AVO does not work Gas-sand amplitude increases with offset AVO can not be used to detect oil sands AVO does not work in carbonates Land AVO is more difficult than marine AVO Vp/Vs is 1.6 for brine sands, 1.8 for dolomites, 1.9 for limestones, and 2 for shales Rp and Rs are readily extracted from R(0) Reality AVO does work under the right circumstances Gas-sand reflection coefficients generally become more negative with increasing of offset. High GOR light oil-saturated rocks may exibit significant AVO anomalies There are some applications The marine short-period multiples are still a problem Vp/Vs varies significantly Rp and Rs can be extracted from R(0) and G if Vp/Vs is kbown

14 Classification R(0)G

15 Classification Figure 5.4. For brine-saturated clastic rocks over a limited depth range in a particular locality, there may be a well-defined relationship between the AVO intercept (A) and the AVO gradient (B). A variety of reasonable petrophysical assumptions (such as the mudrock trend and Gardner’s relationship) result in linear A versus B trends, all of which pass through the origin (B = 0 when A = 0). Thus, in a given time window, nonhydrocarbon-bearing clastic rocks often exhibit a well-defined background trend; deviations from this background are indicative of hydrocarbons or unusual lithologies.

16 Classification Figure 5.5. We propose that the classification of AVO responses should be based on position of the reflection of interest on an A versus B crossplot. First, the background trend within a given time and space window must be defined. This can be done with well control if the seismic data are correctly amplitude calibrated, or with the seismic data itself if care is taken to exclude prospective hidden hydrocarbon-bearing zones. Top of gas sand reflections then should plot below the background trend and bottom of gas sand reflections should plot above the trend. We can classify the gas sand response according to position in the A-B plane of the top of gas sand reflections.

17 Classification

18 Tuning

19 Turbidite system example Figure 5.6. AVO attributes versus net-to-gross Stovas, Landro and Avseth, 2005

20 Seismic section Figure 5.7. Seismic section from offshore Brazil

21 AVO attributes Figure 5.8. AVO attributes sections

22 Inversion of AVO attributes Figure 5.9. AVO attributes inversion from the top reservoir

23 Inversion of AVO attributes (2) Figure AVO attributes inversion from the arbitrary reflection


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