Open fracture clustering: Integrating subsurface and outcrop analogues, Asmari Formation, SW Iran

Abstract

Fracture networks play a significant role in controlling fluid flow in carbonate reservoirs. Here we have utilized a modern and rigorous technique known as normalized correlation count (NCC) to investigate the 1D spatial arrangement and size distribution of conductive opening-mode fractures in the Asmari Formation, a naturally fractured carbonate reservoir in the Zagros Fold and Thrust Belt. To overcome subsurface sampling limitations, here we integrate data from a subsurface fault-related fold called the Gachsaran anticline, and from adjacent outcrops. The Oligo-Miocene Asmari Formation is composed of three units with different lithology, thickness, and stratal geometry. Subsurface and outcrop data from the Asmari Formation have different patterns of spatial arrangements. While the fractures from outcrop anticlines show indistinguishable from random arrangements, the subsurface scanlines from the Gachsaran anticline have various clustering patterns, from self-organized regularly-spaced fracture clustering in the backlimb to externally-imposed clustering in the forelimb. The NW-SE striking fracture set in outcrops and subsurface show clustering. Aperture-size data for all fracture sets are best-fit by log-normal distributions, but sampling biases against the smallest apertures may obscure a power-law aperture-size distribution. Forcing the power-law fit to aperture-size distribution indicates steeper slopes in the backlimb and gentler slopes in the forelimb, which reflect lower and higher strain, respectively. This is consistent with higher tendency of clustering in the forelimb of the anticline which correlates with higher productivity index. Our results demonstrate the ability of NCC to identify hierarchical fracture clusters as well as to distinguish between self-organized and extrinsic organization. The results of quantitative analysis of fracture clustering and patterns in the Asmari Formation provides valuable insights that can aid in the refinement of Discrete Fracture Network (DFN) models and help make informed decisions regarding fractured reservoir development and production.