Use of a Transition Probability/Markov Approach to Improve Geostatistical of Facies Architecture
- LLNL
Facies may account for the largest permeability contrasts within the reservoir model at the scale relevant to production. Conditional simulation of the spatial distribution of facies is one of the most important components of building a reservoir model. Geostatistical techniques are widely used to produce realistic and geologically plausible realizations of facies architecture. However, there are two stumbling blocks to the traditional indicator variogram-based approaches: (1) intensive data sets are needed to develop models of spatial variability by empirical curve-fitting to sample indicator (cross-) variograms and to implement ''post-processing'' simulation algorithms; and (2) the prevalent ''sequential indicator simulation'' (SIS) methods do not accurately produce patterns of spatial variability for systems with three or more facies (Seifert and Jensen, 1999). This paper demonstrates an alternative transition probability/Markov approach that emphasizes: (1) Conceptual understanding of the parameters of the spatial variability model, so that geologic insight can support and enhance model development when data are sparse. (2) Mathematical rigor, so that the ''coregionalization'' model (including the spatial cross-correlations) obeys probability law. (3) Consideration of spatial cross-correlation, so that juxtapositional tendencies--how frequently one facies tends to occur adjacent to another facies--are honored.
- Research Organization:
- Lawrence Livermore National Lab., CA (US)
- Sponsoring Organization:
- USDOE Office of Defense Programs (DP) (US)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 791130
- Report Number(s):
- UCRL-JC-141551
- Country of Publication:
- United States
- Language:
- English
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