Paleokarst reservoirs exhibit complex geologic features comprised of collapsed caves. Traditionally, cave structures are defined using variogram-based methods in flow models and this description does not precisely represent the reservoir geology. Therefore, a quantitative algorithm to characterize and model paleokarst reservoirs based on physical and geological attributes is needed. Multiple-points statistics (MPS) algorithms have been used for modeling complex geologic structures such as channels and fracture networks. Statistics required for these algorithms are inferred from ?gridded? training images but structures like modern cave networks and paleokarst facies are represented by ?point? data sets. Thus, it is not practical to apply ?rigid and gridded? templates and training images for the simulation of such features.
In this study, a new MPS analysis technique and a pattern simulation algorithm are presented to characterize and simulate connected geologic features such as cave networks and paleokarst reservoirs. The algorithm adopts non-gridded training images and non-gridded flexible templates with various distance and angle tolerances for inferring the pattern statistics. The calibrated statistics are implemented in pattern simulation of point data sets representing connected geologic features. In order to verify the proposed technique, it is first implemented to represent connectivity statistics for 3D synthetic data sets. Subsequently, the algorithm is also applied on the Wind Cave and Lechuguilla Cave data sets. Both these cave data sets are used as non-gridded training images for performing MPS analysis. Later, pattern simulation conditioned to limited data is performed constraining the models to the statistics inferred from the training data. This results in simulated point sets and the non-gridded MPS technique successfully reproduced the overall pattern while honoring the conditioning data.
In conclusion, the proposed non-gridded MPS analysis and pattern simulation algorithms are successful at characterizing and modeling connected features that can only be described by point sets. The methodology is practical; it eliminates the gridding procedure and can be directly applied on network data. The algorithm can also be extended to fracture network and hydrology studies.