A Method to Integrate Geological Knowledge in Variogram Modeling of Facies: A Case Study of a Fluvial Deltaic Reservoir
- 1 Faculty of Earth Resources, Key Laboratory of Tectonics and Petroleum Resources, China University of Geosciences, Wuhan China
- 2 Faculty of Earth Resources, Key Laboratory of Tectonics and Petroleum Resources, China University of Geosciences, Wuhan China
Abstract
Variograms are important tools in the spatial distribution of facies and petrophysical properties. Due to the scarcity of subsurface well data, both spatially and quantity wise, variograms representing the data tend to have a lot of uncertainties. In order to reduce uncertainty in variograms, well data can be supplemented with the geological knowledge of the reservoir. This has been demonstrated by various authors in previous works. In their paper “Methodology to Incorporate Geological Knowledge in Variogram Modeling,” A. Bahar and M. Kelkar introduced a methodology to incorporate geological knowledge by studying the energy level of the depositional environment and grain texture. They used these two attributes to determine the relative distance of continuity of the lithofacies and incorporated it in the variogram modeling. In this paper, we introduce another attribute that determines the continuity of lithofacies; the accommodation or deposition space. For illustration purpose, two sets of facies models were constructed: The first using subsurface well data only and the second using well data and geological information of the reservoir. The two sets of models showed significant variation in the property distribution. The first set gave a more random appearance of the facies distribution while the second set gave a more realistic depiction of the depositional environment of the reservoir. We concluded that other than the grain size and the energy level of the depositional environment, another important determinant for continuity in variograms is the knowledge of the depositional space. Incorporating the knowledge of the depositional environment enabled a more accurate estimation of the variogram parameters. This resulted in an improvement in the accuracy of the model.
- Gringarten, E. and Deutsch, C.V. (1999) Methodology for Variogram Interpretation and Modeling for Improved Reservoir Characterization. SPE Annual Technical Conference and Exhibition, Houston, Texas, 3-6 October 1999, SPE-56654-MS.
- Oloo, M.A. and Xie, C.J. (2018) Three-Dimensional Reservoir Modeling Using Stochastic Simulation, a Case Study of an East African Oil Field. International Journal of Geosciences, 9, 214-235. https://doi.org/10.4236/ijg.2018.94014
- Bahar, A., Ates, H., Kelkar, M. and Al-Deeb, M. (2001) Methodology to Incorporate Geological Knowledge in Variogram Modeling. SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, Indonesia, 17-19 April 2001, SPE-68704-MS.
- Deutsch, C.V. and Journel, A.G. (1998) GSLIB Geostatistical Software Library and User’s Guide. In: Applied Geostatistics Series, Oxford University Press, New York.
- Grimes, D.I.F., Pardo-Igúzquiza, E. and Bonifacio, R. (1999) Optimal Areal Rainfall Estimation Using Raingauges and Satellite Data. Journal of Hydrology, 222, 93-108. https://doi.org/10.1016/S0022-1694(99)00092-X
- Margaret Oliver, R.W.J.G. (2009) Geostatistics in Physical Geography. The Royal Geographical Society, London.
- Cressie, N. and Hawkins, D.M. (1980) Robust Estimation of the Variogram. Journal of the International Association for Mathematical Geology, 12, 115-125. https://doi.org/10.1007/BF01035243
- Saeed Soltani-Mohammadi, M.S. (2016) A Simulated Annealing Based Optimization Algorithm for Automatic Variogram Model Fitting.
- Steele, R., Stephen, K. and Lin, X.-Q. (2013) Observed Spatial Statistics of Permeability and the Effects on Fluid Flow; Are We Getting It Right? EAGE Annual Conference & Exhibition incorporating SPE Europec, London, UK, 10-13 June.
- Gribov, A., Krivoruchko, K. and Ver Hoef, J.M. (2005) Modified Weighted Least Squares Semivariogram and Covariance Model Fitting Algorithm. In: Yarus, J.M. and Chambers, R.L., Eds., Stochastic Modeling and Geostatistics, AAPG Computer Applications in Geology, Tulsa, Oklahoma.
- Mcbratney, A.B. and Webster, R. (1986) Choosing Functions for Semi-Variograms of Soil Properties and Fitting Them to Sampling Estimates. European Journal of Soil Science, 37, 617-639. https://doi.org/10.1111/j.1365-2389.1986.tb00392.x
- Introduction to Depositional Environments. https://www.slideshare.net/ay_arain39/deltas-13107361