Abstract
A wide range of methods for geological reservoir modeling has been offered from which a few can reproduce complex geological settings, especially different facies and fracture networks. Multi Point Statistic (MPS) algorithms by applying image processing techniques and Artificial Intelligence (AI) concepts proved successful to model high-order relations from a visually and statistically explicit model, a training image. In this approach, the patterns of the final image (geological model) are obtained from a training image that defines a conceptual geological scenario for the reservoir by depicting relevant geological patterns expected to be found in the subsurface. The aim is then to reproduce these training patterns within the final image. This work presents a multiple grid filter based MPS algorithm to facies and fracture network images reconstruction. Processor is trained by training images (TIs) which are representative of a spatial phenomenon (fracture network, facies...). Results shown in this paper give visual appealing results for the reconstruction of complex structures. Computationally, it is fast and parsimonious in memory needs.
Highlights
Static modeling of complex reservoirs calls for more than two point statistics
We propose a novel multiple grid patternto-pattern algorithm for conditional simulation which is a combination of FILTERSIM fastness and SIMPAT accuracy
Modifications are made to the original FILTERSIM algorithm in several ways of optimizing template size, considering pattern frequency component in database and pattern distance function ranking in candidate bin instead of random selection
Summary
Static modeling of complex reservoirs calls for more than two point statistics. Certain features of these objects such as curvilinearity cannot be expressed via two-point relations [1]. In our previous work [20] one modified FILTERSIM algorithm was proposed for unconditional simulation in which pattern extraction, persisting and pasting steps are modified to enhance visual quality and structures continuity in the realizations. Modifications such as optimum template size selection and additional search steps have considerable improvement in the algorithm performance and produced more visual appealing images compared to FILTERSIM. These changes add marginal computation cost to FILTERSIM because of using pattern frequency concept and yet still much faster than SIMPAT. Results quality and continuity is far better in our proposed algorithm as will be shown shortly
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