Identification, characterization, and up-scaling of pore structure facies in the low permeability reservoirs: Insight into reservoir quality evaluation and sweet-spots analysis

Abstract

The traditional pore structure classification scheme separates the qualitative pore origin classification from the quantitative pore structure parameter classification. This separation prevents a comprehensive understanding and evaluation of the pore structure and characteristics of multi-scale reservoirs. To overcome this limitation, we propose a new pore classification scheme that combines the “geological origin and structure parameters” of pore systems, introducing the term “pore structure facies” (PSF). By considering the genetic types of pores, the pore structure can be qualitatively divided into several categories, each representing different pore genesis. Within each category, the pore structure is further subdivided into subcategories based on pore structure parameters. To validate the effectiveness of the PSF scheme, we focus on the low permeability reservoirs of the Weixinan Depression in the Beibuwan Basin of the South China Sea. We identify and characterize PSF types using a multi-scale approach, including thin section, porosity-permeability, digital image analysis, mercury intrusion test, micro-CT imaging, and simulation. The results reveal that the study area can be classified into three main categories and six subcategories: intergranular homogeneous type (macroporous, mesoporous, and microporous), heterogeneous dissolution-pore type (moderate and strong dissolution), and cementation-induced few-pore type. Significant differences are observed in the two-dimensional and three-dimensional pore structure, petrophysical properties, and pore-scale flow of PSF, leading to significant variations in logging responses. Moreover, we successfully predict and verify the PSF profile using artificial neural network algorithms and field test data. The predicted PSF profile exhibits substantial differences in productivity, with the combination of PSF types serving as an indicator of productivity. We compare the quality of multi-scale reservoirs in the study area using the “point (single well) line (inter-well profile) plane (reservoir)" method based on PSF. Finally, to quantitatively characterize reservoir quality, we propose the Reservoir Sweet-spot Index (RSI), considering the reservoir thickness. By using the boundary of sedimentary facies, we generate a map of sweet-spots reservoirs that provides guidance for future energy exploration and development. The analytical scale of PSF and RSI can be flexibly adjusted, from the heterogeneity within the sand body to the distribution characteristics of a reservoir plane, offering valuable insights into reservoir quality estimation at different scales.