Facies and fracture network modeling need robust, realistic and multi scale methods that can extract and reproduce complex relations in geological structures. Multi Point Statistic (MPS) algorithms can be used to mode...Facies and fracture network modeling need robust, realistic and multi scale methods that can extract and reproduce complex relations in geological structures. Multi Point Statistic (MPS) algorithms can be used to model these high order relations from a visually and statistically explicit model, a training image. FILTERSIM as a pattern based MPS method attracts much attention. It decreases the complexity of computation, accelerates search process and increases CPU per-formance compare to other MPS methods by transferring training image patterns to a lower dimensional space. The results quality is not however as satisfactory. This work presents an improved version of FILTERSIM in which pattern extraction, persisting and pasting steps are modified to enhance visual quality and structures continuity in the realiza-tions. Examples shown in this paper give visual appealing results for the reconstruction of stationary complex struc-tures.展开更多
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) alg...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.展开更多
文摘Facies and fracture network modeling need robust, realistic and multi scale methods that can extract and reproduce complex relations in geological structures. Multi Point Statistic (MPS) algorithms can be used to model these high order relations from a visually and statistically explicit model, a training image. FILTERSIM as a pattern based MPS method attracts much attention. It decreases the complexity of computation, accelerates search process and increases CPU per-formance compare to other MPS methods by transferring training image patterns to a lower dimensional space. The results quality is not however as satisfactory. This work presents an improved version of FILTERSIM in which pattern extraction, persisting and pasting steps are modified to enhance visual quality and structures continuity in the realiza-tions. Examples shown in this paper give visual appealing results for the reconstruction of stationary complex struc-tures.
文摘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.
