A Novel Model for Identifying Effective Pay in Tight Sands

摘要

Identification of effective pay in tight sands is difficult and has always been the central focus of rock physicists and formation evaluationists. Permeability, among other things, is one of the key indicators for reservoir quality. However, many available models based on porosity, as well as the Coates equation based on NMR logs, are not accurate for tight sands, because they do not take into account the pore structure information, such as the connectivity of pore throats and their configuration. We found that the porosity, maximum radius of connected pore throats and the sorting index of all pore sizes are the main three factors for determining the permeability of tight sands. We propose a model combining all these three factors, where the maximum radius is determined by the T_2-Mercury curve transfer and the sorting index by the uniformity coefficient of T_2 distribution. Thus all three factors can be determined by NMR log, making the estimation of permeability wholly based on T_2 logs with higher accuracy than other traditional models. Aside from permeability, anisotropy of porosity and mineral particle sizes, also plays a significant role for effective pay recognition. Cross beddings with different dip angles pose great influences on the permeability and the productivity of the pay zone, the more complex the bedding structure, the smaller the permeability. Image from micro scanning imager provides us with abundant information, including the micro conductivity, grain size, and pore size distribution of the reservoir. By comparing the horizontal and vertical mean value (mathematical or geometric mean value) obtained from the micro scanning imager within a sliding window, we can define an anisotropy coefficient, which is a quantitative indicator of the constitutive property differences in different directions. The smaller the anisotropy coefficient, the higher the T_2 logarithmic mean, indicating that the reservoir tends to have large uniform pore sizes, and more likely to be a productive pay. By combining the information of scanning imager and NMR logs, we establish a quick-look tool for pay zone identification within tight sands intervals. This approach has been shown to be very effective from core analysis and several field examples. It provides an effective means for productive pay zone identification of tight sands.