TECHNICAL FIELD

The present disclosure generally relates to estimating permeability of reservoir rocks, particularly estimating permeability of reservoir rocks based using mercury injection capillary pressure data.

BACKGROUND

Permeability is the ability, or measurement of a rock's ability, to transmit fluids, typically measured in darcies or millidarcies. Formations that transmit fluids readily, such as sandstones, are described as permeable and tend to have many large, well-connected pores. Impermeable formations, such as shales and siltstones, tend to be finer grained or of a mixed grain size, with smaller, fewer, or less interconnected pores. Absolute permeability is the measurement of the permeability conducted when a single fluid, or phase, is present in the rock. Effective permeability is the ability to preferentially flow or transmit a particular fluid through a rock when other immiscible fluids are present in the reservoir (for example, effective permeability of gas in a gas-water reservoir). The relative saturations of the fluids as well as the nature of the reservoir affect the effective permeability. Relative permeability is the ratio of effective permeability of a particular fluid at a particular saturation to absolute permeability of that fluid at total saturation. Calculation of relative permeability allows for comparison of the different abilities of fluids to flow in the presence of each other, since the presence of more than one fluid generally inhibits flow. Understanding the permeability of a formation is important to assessing whether hydrocarbons present in the formation can be produced on a cost-effective basis and managing field operations after production begins.

Mercury injection capillary pressure (MICP) is a technique for measurements of porosity, pore throat size distribution, and injection pressure vs. mercury saturation for many types of rocks. The latter two are correlated to and can be used to estimate permeability.

SUMMARY

This specification describes an approach to modeling the permeability of carbonate reservoir rocks. This approach extracts pore structure and pore fractal dimension information from MICP results. The MICP tests can be performed using a porosimeter (for example, a M9145 Porosimeter commercially available from Grace Instruments of Houston, Tex.). In some cases, this approach uses previously generated MICP data found in special core analysis datasets to extract pore structure and pore fractal dimension information.

In one aspect, methods of estimating permeability of reservoir rocks using mercury injection capillary pressure include: taking a plurality of core samples from a reservoir; performing mercury injection capillary pressure tests to develop a capillary pressure curve for each of the plurality of core samples; determining a pore throat radius (Rd) for each of the plurality of core samples; determining a fractal dimension (D) of each of the plurality of core samples; measuring porosity (∅) of each of the plurality of core samples; determining a composite parameter (β) for each of the plurality of core samples, where

β=∅⁢Rd(D-2);
measuring the permeability (K) of each of the plurality of core samples; performing a regression analysis to determine a relationship of ln(K) as a function of ln(β); and determining the composite parameter (β) for other locations in the reservoir and estimating permeability (K) at the other locations in the reservoir based on the determined relationship.

In one aspect, methods of estimating permeability of reservoir rocks using mercury injection capillary pressure include: receiving mercury injection capillary pressure test data and porosity data for a core sample; determining a fractal dimension (D) for the core sample based on the received mercury injection capillary pressure test data for the core sample; determining a pore throat radius (Rd) for the core sample; determining a composite parameter (β) for the core sample where

β=∅⁢Rd(D-2);
and estimating permeability (K) of the core sample based on a relationship of ln(K) as a function of ln(β) determined by performing a regression analysis data from other core samples from the reservoir.

Embodiments of these methods can include one or more of the following features.

In some embodiments, the core sample is one of a plurality of core samples and receiving mercury injection capillary pressure test data and porosity data comprises receiving mercury injection capillary pressure test data and porosity data for each of the plurality of core samples.

In some embodiments, the plurality of core samples includes at least one core sample from multiple depositional textures present in the reservoir.

In some embodiments, the plurality of core samples includes at least one grainstone core sample, at least one packstone core sample, at least one wackestone core sample, and at least one bindstone core sample.

In some embodiments, methods also include determining the pore throat radius (Rd) for each of the plurality of core samples.

In some embodiments, determining the pore throat radius (Rd) for each of the plurality of core samples comprises determining an entry pressure of the largest pore subsystem from the mercury injection capillary pressure tests and calculating the associated the pore throat radius (Rd) using Washburn's equation.

In some embodiments, methods also include determining a fractal dimension (D) of each of the plurality of core samples.

In some embodiments, determining a fractal dimension (D) of each of the plurality of core samples comprises determining a slope of a curve of ln SHgas a function of ln Pcwhere SHgis mercury saturation and Pcis capillary pressure.

In some embodiments, methods also include measuring porosity (∅) of each of the plurality of core samples.

In some embodiments, methods also include determining a composite parameter (β) for each of the plurality of core samples, where

β=∅⁢Rd(D-2);

In some embodiments, methods also include measuring the permeability (K) of each of the plurality of core samples.

In some embodiments, methods also include performing a regression analysis to determine a relationship of ln(K) as a function of ln(β).

These systems and methods provide a quick way to use MICP data to model permeability of carbonate rocks. The underlying MICP data can be generated using commercially available equipment. For many fields, MICP data is available on special core analysis datasets that are commonly used by oil companies. The parameters used in this approach can be easily extracted from MICP data without requiring fitting parameters. This approach has demonstrated the ability to model permeability and formation factor of heterogeneous carbonate rocks that are formed within various depositional and diagenetic environments. This approach is particularly useful for carbonate rocks because carbonate rocks have complicated pore structures (e.g., moldic porosity, microporosity, vugs and fractures). As a result, no simple correlation can be found for permeability as a function of porosity and the traditional permeability modeling does not work for carbonate rocks. This MICP-based approach provides an effective permeability modeling method for carbonate rocks.

Predicting petrophysical properties of heterogeneous carbonate reservoir rocks is important for many geological and engineering problems including reserve estimation, geological modeling, reservoir simulation, production, water flooding, and enhanced oil recovery. This approach provides a quick and easy way to model permeability of carbonate reservoir rocks, which can be used for complex reservoir-related technical challenges including reserve estimation in complex carbonate reservoirs pay zones with low resistivity and low contrast well log responses.

This approach can predict permeability of carbonate rocks based on MICP data with a high accuracy because it integrates both pore complexity (fractal dimension), pore size distribution (entry pore throat size) and pore volume (porosity). By combining fractal dimension, entry pore throat of largest pore subsystem and porosity extracted from MICP data, this approach can predict both permeability and formation factor. Formation factor (F) is the ratio of resistivity of a rock filled with water (Ro) to the resistivity of that water (Rw) (i.e., F=Ro/Rw). This method is suitable for predicting permeability of heterogeneous carbonate reservoir rocks that have various depositional and diagenetic textures. This method is easier to apply than methods often need many fitting parameters and time-consuming calibrations.

The details of one or more embodiments of these systems and methods are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of these systems and methods will be apparent from the description and drawings, and from the claims.

