Three Statistical Pitfalls of Phi-K Transforms
- Pierre Delfiner (Total S.A.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2007
- Document Type
- Journal Paper
- 609 - 617
- 2007. Society of Petroleum Engineers
- 5.8.7 Carbonate Reservoir, 5.6.4 Drillstem/Well Testing, 5.6.1 Open hole/cased hole log analysis, 1.6.9 Coring, Fishing, 5.5 Reservoir Simulation, 3.3 Well & Reservoir Surveillance and Monitoring, 5.1.5 Geologic Modeling, 4.3.4 Scale, 5.5.2 Core Analysis, 5.5.3 Scaling Methods, 5.5.8 History Matching
- 4 in the last 30 days
- 1,257 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Phi-k transforms are used widely to predict permeability. Some of the difficulties of this exercise are well identified, such as the homogeneity of the population (rock typing), the matching of cores and logs (especially depth matching), and the problem of permeability upscaling. Not so well-known, however, are the pitfalls of a statistical and geostatistical nature that may create significant biases—always in the same direction—an underestimation of permeability.
The passage from Phi to k is performed in three steps: (1) in cored wells, an exponential regression equation is established between core porosity and core permeability k; (2) in uncored wells, log porosity is used instead as input to predict permeability; and (3) the same equation is sometimes used again to populate the cells of a dynamic reservoir model in 3D, where input porosity values are obtained by interpolation.
The core-scale regression equation generally underestimates permeability by at least a factor of 2. The origin of the bias lies in the reverse transformation from logarithmic to arithmetic scale. To avoid this pitfall, a new permeability estimator is proposed, based on the quantile curves of the Phi-k crossplot. This estimator is data driven and does not assume a priori any particular functional relationship between Phi and k, such as an exponential-regression function.
One of the simplest diagnostic tools to check the agreement between log and core porosity is a crossplot of one against the other. In the absence of bias, the points are expected to be distributed along the y = x line. In reality, they either are or they are not, according to which variable is plotted along the x-axis. This apparent paradox is elucidated by bivariate regression theory and related to the difference of investigated volume between core and log data.
Direct input of upscaled cell porosity into an exponential core-scale permeability transform amounts to forcing geometric permeability averaging, which may again lead to serious underestimation of the true upscaled permeability when heterogeneity is significant.
Porosity/permeability correlations are often used to predict permeability and to populate the cells of a dynamic reservoir model. The passage from Phi to k typically involves three steps:
- In cored wells, a regression equation, or transform, is established between core porosity and core permeability, or more exactly, between core porosity and the logarithm of permeability.
- In uncored wells, log-derived porosity is used as input to this equation to predict permeability.
- The same equation is sometimes used again to distribute permeability in 3D at the scale of the cells of a reservoir model, where input porosity values are now obtained by interpolation because most cells are not traversed by a well.
Each step has its own complexities and pitfalls. This paper will mention just a few.
|File Size||2 MB||Number of Pages||9|
Brown, G.C., Hurst, A., and Swanson, R.I. 2000. Swanson's 30-40-30 Rule.The American Association of Petroleum Geologists Bulletin 84(12): 1883-1891.
Chilès, J.P. and Delfiner, P. 1999. Geostatistics: Modeling SpatialUncertainty. John Wiley & Sons: New York City.
Knecht, L., Mathis, B., Leduc, J.P., Vandenabeele, T., and Di Cuia, R. 2004.Electrofacies and Permeability Modeling in Carbonate Reservoirs using ImageTexture Analysis and Clustering Tools. Petrophysics 45 (1):27-37.
Matheron, G. 1967. Eléments pour une théorie des milieux poreux.Masson : Paris.
Noetinger, B. and Haas, A. 1996. Permeability Averaging for Well Testsin 3D Stochastic Reservoir Models. Paper SPE 36653 presented at the SPEAnnual Technical Conference and Exhibition, Denver, 6-9 October. DOI:10.2118/36653-MS.
Noetinger, B. and Zargar, G. 2004. Multiscale Description and Upscaling ofFluid Flow in Subsurface Reservoirs. Oil & Gas Science andTechnology—Rev. IFP 59 (2): 119-140.
Pallatt, N., Wilson, J., and McHardy, W.J. 1984. The Relationship Between Permeabilityand the Morphology of Diagenetic Illite in Reservoir Rocks. JPT36 (12): 2225-2227. SPE-12798-PA. DOI: 10.2118/12798-PA.
Pearson, E.S. and Tukey, J.W. 1965. Approximate Means and StandardDeviations Based on Distances Between Percentage Points of Frequency Curves.Biometrika 52 (3-4): 533-546.
Renard, P. and de Marsily, G. 1997. Calculating Equivalent Permeability: aReview. Advances in Water Resources 20 (5-6): 253-278.
Soeder, D.J. 1986. LaboratoryDrying Procedures and the Permeability of Tight Sandstone Core.SPEFE 1 (1): 16. SPE-11622-PA. DOI: 10.2118/11622-PA.
Worthington, P.F. 2004. The Effect of Scale on the Petrophysical Estimationof Intergranular Permeability. Petrophysics 45 (1): 59-72.