Applications of Geophysical Inversion and Imaging Part 5 – AVO Case Studies. Gulf of Mexico Case Study. We will now look at a Gulf of Mexico case study and consider several approaches to dealing with poorly corrected gathers.
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Applications ofGeophysical Inversion and ImagingPart 5 – AVO Case Studies
The figure above shows the input gathers with the sonic log inserted at Xline 100. Note that the gathers are noisy, and the target event, indicated by the blue line, is badly corrected.
The figure above shows a super-gather, or common offset stack, run on the previous gathers using a 3 x 3 mix. The data is cleaner, but the event is still badly corrected.
The figure above shows an A+B AVO analysis run on the previous super-gathers, with the anomaly shown below the pick at the well. Despite the poor NMO correction, the anomaly is visible.
The figure on the left shows the scaled Poisson’s ratio plot (A+B) averaged over a 30 ms window below the target horizon, using an amplitude envelope attribute. Notice the clear outline of the anomalous zone.
However, could we have done better if we had done a better job of RNMO?
Top
Base
These are the angle gathers from the Gulf of Mexico, after a trim static. Angles range from 0 to 60 degrees. The target layer is annotated at right.
These displays show the results of fitting the Aki-Richards equation, using 2 and 3 terms, to the event highlighted on the previous slide.
Note that the equation for 2 terms begins to deviate from the seismic picks after about 45 degrees.
3 Term
2 Term
Base
Top
The Scaled Poisson’s Ratio maps for the two results are different, even though each depends only on A and B.
Because of the good seismic data with angles to 60 degrees, we expect the 3-term result to be more reliable.
2 Term
3 Term
Recall that both forms of the Aki-Richards equation (ABC and Fatti) can allow us to estimate density variations.
Using the original A,B,C form, we see that:
This means that if we can estimate all three coefficients, we can generate a density attribute volume.
That can be very valuable since density is a direct measure of hydrocarbon saturation. This could solve the “fizz water” problem.
However, the third coefficient can be very noisy since it depends on the far angle data (>45 degrees), and is very sensitive to noise.
The map of the density term highlights the same areas as the delta VS.
DVP/VP is perhaps best in this case: the hydrocarbon anomaly is associated with a strong change in VP.
To try to analyze the target horizon correctly on the poorly corrected data before trim static, we picked the trough over the target at each gather, using an automatic picking program. Note that some of the long offset picks are miss-picked.
The figure above shows a display of the picks below the gathers, where an AVO curve has been fitted. By using a robust fitting method, we have been able to avoid the miss-picks.
The figure on the left shows the scaled Poisson’s ratio plot (A+B) from the picked target horizon. Notice the better definition of the anomalous zone when compared back to the standard AVO analysis. Note the reversed scale since we did not need to take the absolute value here.
A comparison between the two maps will be shown in the next slide.
(a)
(b)
The figures above show (a) the pseudo-Poisson’s ratio plot from the standard AVO analysis, corrupted by poor NMO, and (b) the target-oriented pseudo-Poisson’s ratio analysis found by picking the horizon.
This case study comes from papers bySmith and Gidlow(Geophysical Prospecting, November, 1987) and Fatti et al (Geophysics, September, 1994).
We will not review their complete papers, but simply summarize their approach and look at the 2D and 3D results.
Their papers shows an example of the fluid factor methodapplied to a actual reservoir.
We will start with the theory, then show a modeled example, the 2D example, and finally the 3D example.
Smith and Gidlow (1987) proposed the “fluid factor” stack by using Castagna’s mudrock line, as follows:
This was modified byFatti et al. (1994) (Smith was the second author) in the following way, and is the approach used to compute fluid factor in this paper:
(a) Modeled logs. Note false anomaly at 2.5 seconds.
(b) Results of analysis. Only DF showed the true anomaly.
Smith and Gidlow (1987)
Cross plot of shear velocity (W) against P-wave velocity (V) for the 2D example
Smith and Gidlow (1987)
(a) P-wave reflectivity, DVP/VP
(b) S-wave reflectivity, DVS/VS
Smith and Gidlow (1987)
(b) Fluid Factor section
(a) Pseudo-Poisson’s Ratio
Gas sand at 2.0 s
Smith and Gidlow (1987)
Depth structure contour map interpreted from 3D seismic data.
Fatti et al (1994)
Maximum value of amplitude envelope from conventional seismic.
Fatti et al (1994)
Fluid factor amplitude from top-of-gas event.
Fatti et al (1994)
Fluid factor amplitude from base-of-gas event.
Fatti et al (1994)
Sum of fluid factor amplitude maps from top-of-gas and base-of-gas events.
Fatti et al (1994)
The fluid factor method was able to identify the anomaly on the model example much more clearly than with pseudo-Poisson’s ratio.
On the 2D South African example, the anomaly was again much more clearly indicated using the fluid factor.
On the 3D South African example, the fluid factor method was compared to traditional post-stack amplitude analysis. The fluid factor approach did a much better job of identifying the anomaly.
Now let us finish with a case study from a paper by Mark Gregg and Charles Bukowski (Leading Edge, November, 2000):
This paper shows a very practical example of the application of AVO to a mature basin.
The exploration objective was the clastic Oligocene Vicksburg formation in South Texas.
This has produced more than 3 trillion ft3 of gas since the 1920’s, but not much AVO work has been reported.
The authors believe the lack of AVO application comes because “the Vicksburg trend is not a typical amplitude-supported play”.
The motivation for using AVO came from results like those shown on the left.
Using the conventional post-stack data, it is difficult to distinguish Gas from Wet sand before drilling.
Prior to AVO analysis, the authors had drilled one commercial gas well, one non-commercial gas well, and three dry holes.
These curves from the gas discovery well show both a Gas and a Wet zone.
The change in acoustic impedance is small but the change in Poisson’s ratio is large.
This suggests a class 2 AVO anomaly.
Synthetic modeling confirmed the expected class 2 response.
The data was re-processed to include nonhyperbolic moveout. This turned out to be critical, as the figure shows.
A very useful indicator is the Near and Far Angle Stack.
Note that the Gas sand shows its brightest response on the Far Angle stack, as expected for the class 2 behavior.
The authors used the Far Angle Stack as the main tool for searching for new anomalies.
This is the first drilled anomaly.
100 ft gross interval with 72 ft of net pay, producing initially 3 million ft3 of gas per day.
Since the anomaly is not visible on the conventional stack, this would not have been drilled without the AVO analysis.
A second anomaly was identified by interpreting the far-angle stack using Landmark’s Earthcube software.
This had not been identified before AVO, because of the poor quality of the convention stack. This was presumed to be because of the small acoustic impedance contrast.
Note that there appear to be multiple anomalies at the prospective level.
The drilled well enountered 2 pay zones.
Upper zone: gross thickness pf 54 ft, with 28 ft net pay.
Lower zone: gross thickness of 214 ft with 69 ft net pay.
Initial production rate was 5.3 million ft3 with estimated ultimate recovery of 14 billion ft3.
Two more successful wells are shown here.
This is an unsuccessful result. The drilling encountered 105 ft of clean, low-gas-saturated sand at the anomaly.