AAPG ANNUAL CONFERENCE AND EXHIBITION
Making the Next Giant Leap in Geosciences
April 10-13, 2011, Houston, Texas, USA
Capturing Porestructures with Digital Image Analysis for a Quantitative Correlation to Physical Properties
(1) University of Miami, Miami, FL.
(2) Statoil ASA, Stavanger, Norway.
(3) ExxonMobile, Houston, TX.
Porous carbonates typically contain a wide range of pore sizes and shapes that are difficult to capture with existing pore type classifications. These classifications are insufficient to relate complex pore structures to physical properties. The analysis of pores on digital photomicrographs is an objective, repeatable, and quantitative methodology that analyzes the size distribution and describes the shape of the pore system. Pore shape parameters derived using digital image analysis (DIA) capture complicated pore structure and help explain the variability of physical parameters such as acoustic velocity, electrical resistivity (formation factor), and permeability.
The pore size distribution is captured using the parameter Dominant Pore size (DOMsize), a quantitative measure taken from the cumulative size distribution. The complexity of the pore system is measured by the perimeter over area (PoA). PoA (the 2 dimensional equivalent to the specific surface) captures of the overall intricacy of the pore system. Both parameters show high correlation to variations in physical properties.
For example, it has been shown that at a given porosity, samples with simple large pores have higher velocity than samples with an intricate pore network dominated by small pores. This trend is reflected in DIA parameters.
Variations in the formation factor are also related to the pore size and shape. This can be documented in plots of porosity vs formation factor with DIA parameters superimposed. Samples with a low value of PoA and high DOMsize have relatively high formation factor values, whereas samples with high values of PoA and low DOMsize have low formation factor values. This indicates that for a given porosity samples with simple large pores have higher resistivity than samples with an intricate pore network dominated by small pores.
Theoretically, both pore size and specific surface influence permeability. Samples with low permeability at a given porosity have high values of PoA and low values of DOMSize and vice versa. A caveat, however, are moldic rocks which can have high DOMsize with low permeability due to poor connectivity of the large pores.
Both, pore size and pore system intricacy as defined by digital image analysis are highly correlated to acoustic velocity, permeability, and electrical resistivity. A combination of these pore shape parameters with porosity is capable of substantially improving inverting pore structure from down hole logging data.