Abstract
Classifying rock facies and estimating permeability is particularly challenging in Microporous dominated carbonate rocks. Reservoir rock types with a very small porosity range could have up to two orders of magnitude permeability difference resulting in high uncertainty in facies and permeability assignment in static and dynamic models. While seismic and conventional porosity logs can guide the mapping of large scale features to define resource density, estimating permeability requires the integration of advanced logs, core measurements, production data and a general understanding of the geologic depositional setting. Core based primary drainage capillary pressure measurements, including porous plate and mercury injection, offer a valuable insight into the relation between rock quality (i.e., permeability, pore throat size) and water saturation at various capillary pressure levels.
Capillary pressure data was incorporated into a petrophysical workflow that compares current (Archie) water saturation at a particular height above free water level (i.e., capillary pressure) to the expected water saturation from core based capillary pressure measurements of various rock facies. This was then used to assign rock facies, and ultimately, estimate permeability along the entire wellbore, differentiating low quality microporous rocks from high quality grainstones with similar porosity values. The workflow first requires normalizing log based water saturations relative to structural position and proximity to the free water level to ensure that the only variable impacting current day water saturation is reservoir quality.
This paper presents a case study where this workflow was used to detect the presence of grainstone facies in a giant Middle Eastern Carbonate Field. Log based algorithms were used to compare Archie water saturation with primary drainage core based saturation height functions of different rock facies to detect the presence of grainstones and estimate their permeability. Grainstones were then mapped spatially over the field and overlaid with field wide oil production and water injection data to confirm a positive correlation between predicted reservoir quality and productivity/injectivity of the reservoir facies. Core based permeability measurements were also used to confirm predicted permeability trends along wellbores where core was acquired.
This workflow presents a novel approach in integrating core, log and dynamic production data to map high quality reservoir facies guiding future field development strategy, workover decisions, and selection of future well locations.
Automated production data integration for energy-oriented process chain design