TITLE:
Facies and Fracture Network Modeling by a Novel Image Processing Based Method
AUTHORS:
Peyman Mohammadmoradi
KEYWORDS:
Filter-Based; Image Processing; Pattern; Training Image; Entropy Plot; MPS
JOURNAL NAME:
Geomaterials,
Vol.3 No.4,
October
25,
2013
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.