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Quantifying Seismic Reflectivity

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Presentation on theme: "Quantifying Seismic Reflectivity"— Presentation transcript:

1 Quantifying Seismic Reflectivity
Based on UH Dissertation by Dr. Jenny Zhou – Former AGL RA A Second Look at the Fluid Factor My presentation is “ Pore=Fluid ….” My co-author is a former student and now and employee at Schlumberger Fred Hilterman Distinguished Research Professor – University of Houston Chief Scientist – Geokinetics Data Processing and Interpretation April 28, 2010

2 George Smith & Maurice Gidlow
Birth of Fluid Factor George Smith & Maurice Gidlow South Africa 1. Since the lambda-rho attributes were very similar to Poisson impedance for pore fluid quantification, we will only show the PI results.

3 Fluid Factor Review RC()  NIP/cos2() – 2 NIS sin2() Class 1 AVO
2.9 Before we examine pore-fluid quantification, let’s review one of the first pore fluid indicators, Smith and Gidlow’s Fluid Factor The geologic model represents a reservoir with a Class 1 AVO response. This can be seen in the seismic stack response for the water leg is larger than stack response for the oil or even the gas portion of the reservoir, which constitutes a Class 1 AVO anomaly. Remembering there are CMP gathers that can be analyzed, we go to Stack 3.0 Smith and Gidlow; Fatti et al. Generate NIP and NIS from AVO inversion RC()  NIP/cos2() – 2 NIS sin2() NIP = P-wave normal incidence NIS = S-wave normal incidence

4 Fluid Factor Review NIP NIS Class 1 AVO Stack
Fluid Factor: F = NIP -  NIS 2.9 Stack 3.0 2.9 3.0 NIP Brine saturated: F = 0 Gas Saturated: F = NIPGAS- NIPWET 2.9 3.0 NIS

5 Can F discriminate oil from gas ?
Fluid Factor Review 2.9 Stack 3.0 2.9 NIP NIS Gas Oil 3.0 Can F discriminate oil from gas ?

6 Calibrating Fluid Factor to
Well-Log Data 1. Since the lambda-rho attributes were very similar to Poisson impedance for pore fluid quantification, we will only show the PI results.

7 Fluid Factor from Normal Incidence
151 wells from South Marsh Island NIOIL = NIWET R2 = .94 NIGAS = NIWET R2 = .83 Regression Equations (NIGAS-NIWET) NIOIL NIWET = NIGAS NIWET = Wet Oil The NI for the three pore fluids is shown in the plot. What amazes many is the excellent regression correlation that the Gas and oil NI has to the brine-saturated NI. In the middle of the right slide, I have shifted the NI(WET) term to the left of the regression equation. Note that the coefficient on the NI(WET) is very close to 1, so that we see the fluid factor for gas is essentially constant at a value of and that of oil is , we find that the NI values by themselves vary considerably … but theoretically the fluid factors for brine and gas change vary slowly. The data include a wide range of sand porosities and encasing shale properties. Now how are the seismic fluid factors calculated? Gas NIHYDROCARBON-NIWET  Fluid Factor NI varies significantly for gas- and brine-saturated sand … but (NIGAS-NIWET) changes little. Reservoir porosity: % Encasing shale: m/s How is Fluid Factor extracted from seismic data?

8 Fluid Factor from NIP & NIS
F(t) ≡ NIP(t) -  NIS(t) (Smith and Gidlow) F  In wet zones F  (NIP,GAS-NIP,WET) At top of reservoir F(t) ≡ NIP(t) – 0.72 NIS(t) therefore … E{FWET} = 0 Based on horizon pore-fluid projection Linear Equation NIP=0.72NIS-0.03 Wet Oil Gas Fluid Factor = NIP NIS Wet Oil Gas The computation of the fluid factor was given to us by Smith and Gidlow who rearranged the AVO equation so that on inversion we obtain the intercept value was related to the p-wave NIP and the slope related to the shear-wave NIS. The gamma factor can be estimated from the well-log data by noting the regression equation in the plot of NIP versus NIS. To get the fluid factor the NIP is moved to the right side of the regression equation and the left side becomes the fluid factor as shown on the bottom of the right figure. This is axis rotation is often referred to as a fluid projection. What is interesting is that the variable NI for wet and gas represent changes in porosity from 10% to 35% … yet the fluid factor hardly changes. Let’s do the similar measurements regression analyses for PI and lambda-rho as shown on the next slide. The gamma factor was developed so that the fluid factor was zero at the top of a wet zone and equal to (NIPgas –NIwet) when on the top of a gas reservoir. 151 wells in South Marsh Island

9 Unique Properties of Fluid Factor
1. Since the lambda-rho attributes were very similar to Poisson impedance for pore fluid quantification, we will only show the PI results.

10 Fluid Factor: Boundary or Layer Property?
NIGAS – NIWET = F Gas Sand Shale S R NIGAS Wet Sand Shale S R NIWET Wet Sand Gas Sand S R F  - NIGWC - - The three cartoon drawings depict the definition of the fluid factor. Under the right cartoon, we indicate that the fluid factor is actually related to the reflection from a hydrocarbon/water contact … gas/water as indicated during this talk. And as the following equation illustrates, the fluid factor is independent of the encasing shale. The fluid factor acts as a layer property … in some fashion similar to PI and lambda-rho. In fact, let’s relate Fluid factor to the other two attributes next. NIGWC = (AIWET SAND–AIGAS SAND) (AIWET SAND+AIGAS SAND) F relates to gas/water contact -NIGWC … not encasing shale.

11 Fluid Factor: 9-m Wet Thin Bed
VP = 2280 m/s VP= 2250 m/s VP = 3600 m/s VP = 3490 m/s NIP NIS Seismic Section: Fluid Factor = NIP – 0.55 NIS F

12 Fluid Factor: 9-m Gas Thin Bed
11.1 ms Two-way time 5.3 ms 109% increase in two-way time VP = 2280 m/s VP = 2280 m/s VP= 1628 m/s VP = 3370 m/s VP = 2280 m/s VP = 3490 m/s NIP NIS Seismic Section: Fluid Factor = NIP – 0.55 NIS F Approximately 109% increase in amplitude

13 Thin Bed: Fluid Factor vs. NI Normal-Incident Reflectivity
NI = f( VP1, VP2, Rho1, Rho2) * 2 t/T t = thin-bed traveltime T = wavelet period Wet ……..… F = * 2 t/T Oil, Fizz…… F = * 2 t/T Gas ……….. F = * 2 t/T Fluid Factor Fluid factor independent: shale properties reservoir porosity upper shale  lower shale

14 Field Data 1. Since the lambda-rho attributes were very similar to Poisson impedance for pore fluid quantification, we will only show the PI results.

15 GOM Oil and Gas Reservoir Reservoir exhibits fault shadow
7.5 km Oil Gas 1 s 2 s 3 s -3000 m Reservoir exhibits fault shadow velocity anomaly The seismic data is from a Tertiary section the GOM. The reservoir production started in the early 1970s and the seismic was shot in the 1996. There are 10 wells available for this study … however none had both sonic and density curves through the reservoir. Synthetic ties to seismic were questionable at the best and trace inversions based on wavelet control were problematic. In addition, a strong lateral change in velocity occurs that is associated with the reservoir trapping fault. A velocity anomaly is evident when the reservoir time structure is compared to the depth map generated from the well-log depths. Well Production: Early 1970s Seismic Data: as OBS Seismic Processing: 2008 Seismic data from Fairfield Industry, Inc.

16 AVO Inversion –NIP & NIS
Seismic NIP Seismic NIS Fault 400 Fault 1500 WET WET 4.2 miles -200 The time map of the reservoir is shown on the left slide while the reservoir depth map derived from well control is on the right. Out of the ten wells, only three are still producing. The two current oil producers are labeled 8 and 9 while the gas well is labeled as 10. Numbers 1-7 were producing oil but have since been shut in. Note velocity problem as wells 5, 6 and 9 are on the same time contour but are different depth contours. An AVO inversion of the CMP gathers using Smith and Gidlow’s equation was performed to yield the shown NIP and NIS horizon maps. The amplitude values for the NIP and NIS maps are relative but are not absolute in that they do not correlate to the values derived from well-log data. In the northern portion of the seismic maps there are dashed blue boxes where the interpretation indicates that the reservoir sand is wet, brine saturated. The NIP and NIS are calibrated with regional well-log statistics. -1500 -800 4.6 miles Producing gas well Abandoned oil well Producing oil well Zhou 2009

17 Fluid-Factor Projection Line Calibrated Fluid Factor
NIP(2)= NIS(2) R2=0.8408 NIP = 0.33 NIS R2 = 0.84 0.02 Calibrated NIP -0.04 The previous seismic map values for NIP and NIS were calibrated to well-log NIP and NIS values using the shown standard deviations and means from the seismic and well-log statistics. The well-log calibrated NIP and NIS maps were crossplotted as shown in the slide. Remember the wet area was defined by the blue box in the northern part of the seismic survey. The blue regression line is a fluid-projection line that allows us to calculate the fluid factor which uses the regression equation coefficients as shown on the bottom of the slide on the right side. The color bar now represents calibrated well-log fluid factors. With a little bit of Bayes’ Theory, the fluid-factor map can be classified into wet, oil and as zones as examined next. Fault Wet area Whole survey -0.08 Fluid Factor = NIP -.33 NIS Calibrated NIS Producing gas well Abandoned oil well Producing oil well Zhou 2009

18 Fluid Factor Prediction of Pore Fluid
Producing gas well Abandoned oil well Producing oil well - 0.02 Wet sand Oil sand Gas sand 9900 1 2 3 4 5 6 7 8 9 10 Fault Depth Contours Fluid Factor On the left is the fluid factor classification based on the Baysian cutfoff values. On the right, is the attribute Poisson Impedance that has been developed in a similar way as the fluid factor for this study. Remember though, Poisson impedance requires knowledge of the seismic wavelet and the low-frequency trend. Since this is real data and we don’t know the exact answer, we have used a somewhat subjective evaluation. How well does out results correspond to the known well control, especially the current producing wells. We find for the fluid factor … All 3 producing wells are located in correct prediction for as, oil and wet … only one in PI and finally, the gas zone is correct using the fluid factor but incorrect for the Poisson impedance. Let’s briefly look at how the fluid factor would fair with more consolidated rocks. 3 producing wells correctly located Gas zone correctly located Gas and oil zones generally tie depth contours Calibrated fluid factor is an accurate pore-fluid discriminator. Zhou 2009

19 Summary: Fluid-Factor Properties
… independent of shale properties above or below reservoir. … independent of reservoir porosity. 3. … independent of wavelet shape. Read 4. … related to NI of hydrocarbon/water contact. 5. … function of thin-bed traveltime and pore fluid.

20 Field Study Conclusions
NIP vs. NIS horizon crossplot provides … E{FWET} = 0. 2. Regional well-log curves provide … Seismic to well-log amplitude calibration. Read 3. With questionable wavelet and low-frequency control, F was better pore-fluid discriminator than PI or .

21 Thanks for your attention.
1. Since the lambda-rho attributes were very similar to Poisson impedance for pore fluid quantification, we will only show the PI results.

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