Abstract
An analysis of spatial resolution is incorporated into an efficient model calibration approach with multi-scale data integration to examine the reliability of the estimated solution. The resolution is a measure of the degree of averaging of the local-scale (grid block) permeabilities during parameter estimation via inverse modeling. For a given set of data, it indicates the regions where our estimate is well constrained. By examining the spatial resolution in time-lapse seismic data integration, we can quantitatively evaluate the relative contribution of pressure and saturation changes on the calibrated permeability field. We illustrate this concept using synthetic and field applications. The synthetic example is a 5 spot pattern, where the time-lapse seismic data are incorporated as inferred pressure and saturation changes. The field example involves waterflooding of a North Sea reservoir with multiple seismic surveys. The results demonstrate that the analysis of spatial resolution provides quantitative information on our ability to estimate the subsurface heterogeneity. It is found that integration of seismic data based on inferred pressure changes better determine the barriers to the flow (e.g., low permeability areas), while calibrating the model based on saturation changes provides complementary information to identify the channels (e.g., high permeability regions).
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Abbreviations
- \(\tau\) :
-
Time of flight along streamlines
- \(\psi\) :
-
Streamline trajectory
- s :
-
Slowness
- \(\vec{\nu }\) :
-
Interstitial velocity
- \(\lambda\) :
-
Mobility
- \(k\) :
-
Permeability
- \(\phi\) :
-
Porosity
- \(S_{\text{w}}\) :
-
Water saturation
- \(F_{\text{w}}\) :
-
Fractional flow of water
- \(P\) :
-
Hydrostatic pressure
- \(P_{\text{eff}}\) :
-
Effective pressure
- \(P_{\text{ext}}\) :
-
Lithostatic pressure
- \(\Delta P\) :
-
Pressure drop along streamlines
- \(\delta m\) :
-
Model parameter change
- \(k_{\text{HM}}\) :
-
Bulk modulus of the Hertz–Mindlin formula
- \(K_{\text{ma}}\) :
-
Bulk modulus of the matrix
- \(K_{\text{fr}}\) :
-
Bulk modulus of the porous rock frame
- \(K_{\text{f}}\) :
-
Bulk modulus of the pore-filling fluids
- \(K_{\text{sat}}\) :
-
Bulk modulus of the fluid saturated rock
- \(\rho_{\text{sat}}\) :
-
Density of the fluid saturated rock
- \(G_{\text{fr}}\) :
-
Shear modulus of the porous rock frame
- \(V_{\text{p}}\) :
-
Compressional (p-wave) velocity
- \(Z_{\text{p}}\) :
-
Acoustic (p-wave) impedance
- \(R\) :
-
Resolution model
- \({\text{COV}}_{\text{m}}\) :
-
Model covariance
- \({\text{COV}}_{\text{d}}\) :
-
Data covariance
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The authors acknowledge the sponsors of Model Calibration and Efficient Reservoir Imaging (MCERI) at Texas A&M University.
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Hetz, G., Datta-Gupta, A. Integration of Time-Lapse Seismic and Production Data: Analysis of Spatial Resolution. Transp Porous Med 134, 679–705 (2020). https://doi.org/10.1007/s11242-020-01463-4
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DOI: https://doi.org/10.1007/s11242-020-01463-4