Predicting fluid flow in reservoirs: analysis of fracture clustering in outcrop analogues

Abstract

Researchers have used lacunarity, a parameter that quantifies scale-dependent clustering in patterns, to differentiate fracture networks that belong to the same fractal system. In a previous study, the authors showed that lacunarity is efficient in representing connectivity and fluid flow in fractal-fracture models with the same fractal dimension. The objective of this research is to investigate if the concepts thus developed are applicable to outcrop analogues that are representative of subsurface fractured reservoirs. A set of nested fracture networks belonging to a single fractal system but mapped at different scales and resolutions is considered in this study. Lacunarity and connectivity values of these maps are evaluated using geospatial data analysis techniques. Fracture continuum (FC) models are built from these fracture maps and a streamline simulator, TRACE3D, is used to flow simulate these maps. Results show that although the fractal dimension of these maps is same, differences exist in the values of lacunarity, percolation connectivity and also fluid recovery values. It is further noted that the clustering, connectivity and fluid recovery values can be pairwise correlated very well for these natural fracture maps. Thus, the overall results indicate that connectivity in fracture maps, and hence their flow properties, are controlled by lacunarity or scale-dependent clustering attributes. Therefore, there could be novel applicability of the lacunarity parameter in calibrating discrete fracture network (DFN) models with respect to connectivity in natural fracture maps and the prediction of flow behaviour in fractured reservoirs. Thematic collection: This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at: https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows