Elastic attribute analysis using rock physics and seismic modelling in gas storage monitoring

Vidal, S.; Jardin, Anne; Huguet, F.

doi:10.3997/2214-4609.201406721

Cited by 2 publications

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“…Several researchers have found that using a power of 1/5 instead of 1/3 in the equations (Landrø et al . 2001; Vidal et al . 2001) gives a better fit with real data.…”

Section: Discussionmentioning

confidence: 99%

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Optimal use of PP and PS time‐lapse stacks for fluid–pressure discrimination

Stovas¹,

Landrø

2004

Geophysical Prospecting

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A B S T R A C TThe combined use of time-lapse PP and PS seismic data is analysed for optimal discrimination between pressure and saturation changes. The theory is based on a combination of the well-known Gassmann model and the geomechanical grain model derived by Hertz and Mindlin. A key parameter in the discrimination process is the opening angle between curves representing constant changes in PP and PS reflectivity plotted against pressure and saturation changes. The optimal discrimination angle in the pressure-saturation space is 90 • and this is used to determine optimal offset ranges for both PP and PS data. For typical production scenarios, we find an optimal offset range corresponding to an angle of incidence of 25-30 • , for both PP and PS data. For gas we find slightly different results. This means that conventional survey parameters used in marine multicomponent acquisition should be sufficient for the purpose of estimating pressure and fluid saturation changes during production. I N T R O D U C T I O NDuring the last five years, several methods for fluid-pressure discrimination from time-lapse seismic data have been suggested. Lumley and Tura (1999) proposed an inversion method for discrimination between pressure and saturation changes based on time-lapse AVO data. The first step is to invert for relative changes in P-and S-wave impedances from the relative changes in the time-lapse AVO data. A second optional step is to use log data to obtain absolute P-and S-wave impedances from the relative changes. Finally, timelapse changes in impedances are related to time-lapse changes in reservoir properties using impedance cross-plotting. Landrø (1999, 2001) derived explicit equations relating pressure and saturation changes directly to time-lapse amplitude changes in near-and far-offset stacked data. The input needed for the method consists of empirical rock physics equations relating changes in P-and S-wave velocities and densities to changes in pore pressure and saturation. An extension of this method * E-mail: alexey@ipt.ntnu.no including the use of PS-converted stacked data has been presented by Landrø et al. (2003). The effect of anisotropic rocks on pressure-fluid discrimination has been studied by Stovas and Landrø (submitted to Geophysics). For saturation changes, the Gassmann equations are commonly used. For pressure changes, ultrasonic core measurements or geomechanical models, such as the Hertz-Mindlin model (Mindlin 1949), are used to obtain this information.A complementary method to obtain quantitative information about reservoir property changes during production is to measure the shift in the two-way traveltime within a given reservoir section. A practical example of how this method can be used to give additional information about pressure and saturation changes in a reservoir segment has been given by Landrø, Digranes and Strønen (2001). Measurements of traveltime shifts can be used to estimate change in, for instance, pay thickness or the amount of porepressure change. Combined with estimate...

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“…Several researchers have found that using a power of 1/5 instead of 1/3 in the equations (Landrø et al . 2001; Vidal et al . 2001) gives a better fit with real data.…”

Section: Discussionmentioning

confidence: 99%

“…In the original Hertz–Mindlin theory, the degree of the root, η, in and was 3. Vidal, Jardin and Huguet (2001) found η= 5.6 for P‐waves and η= 3.8 for S‐waves for gas sands. With a small change in pressure, and are given by and For simplicity we use η= 5 in all our computations.…”

Section: Use Of the Hertz–mindlin Modelmentioning

confidence: 94%