Field-scale and site-specific permeability of sandstone reservoirs using a stabilized Monte Carlo algorithm: A case study

Abstract

The permeability of sandstone varies significantly from site to site at the field scale. Well-established methods for permeability estimation depend on laboratory-measured parameters, which are available only at sparse locations. To overcome such dependency, we have developed a method to estimate sandstone’s site-specific and field-scale permeability using well log and interconnected porosity data, respectively. We have considered sandstone as a mono-dispersed porous medium with monofractal pores and simulated them using the fractal theory-based acceptance-rejection Monte Carlo algorithm. Stabilization of the algorithm through an error convergence scheme generated repeatable and reliable permeability values. We tested this developed method on cores from various formations representing clean/shaly sandstones and a few sandstone samples contaminated by carbonates. Compared to laboratory-measured permeability, the estimates are within one-order magnitude error bounds for clean/shaly sandstones and higher for samples that are either contaminated by carbonates or have higher pore volume normalized surface area. We applied the method to infer field-scale permeability trend to explain the unexpected movement of hydrocarbons in the Kalol reservoir lying in the Balol oil field of Cambay Basin India, where the in-situ combustion enhanced oil recovery process is implemented. The field-scale permeability trend could explain the northward movement of the hydrocarbons to the adjacent oil field, Lanwa. It was also observed in the production data of the Lanwa field that the in-situ combustion in Balol has benefited oil production in its wells.