Permeability modeling and permeability prediction has always been a critical phase in building geological models. In a major Abu Dhabi offshore oil field, several reservoir characterization and evaluation studies have been conducted during the last five years. The estimation of permeability in each well is required to identify and model reservoir flow units in the field, Rock types are identified from cored wells using thin section and conventional core analysis (rock fabric, porosity, permeability,) in parallel with relating it to capillary pressure curves. In non-cored wells (90% of total wells), a backward model of rock type classification is achieved through the utilization of well logging data which is proved to provide (1) Porosity & (2) Lithology information using Neutron, litho-Density and Sonic tools, therefore, Permeability from logs is the missing information to make the log rock typing backward model working.

In these studies, several innovative approaches of permeability estimations have been developed in-house and used or declined in different stages. Two of these approaches will be thoroughly explained to explore the strengths and weaknesses of each one of them with the reasons of being working in some formations but not others, where different relations between Core permeability and Log responses in different formations is driven by rock properties.

In the first approach, the invasion profile permeability index from Electrical Resistivity logs has been used to honor the general relation between porosity and permeability, in which six different cases and conditions has been considered to correct for the universal equation developed in this approach from logging data of Electrical Resistivity, these consideration are:

  1. gas zone,

  2. oil & transition zone,

  3. water zone,

  4. fractured area

  5. drilling fluids

  6. facies quality.

This permeability estimation approach is called Universal Rock Permeability (UROK).

In the second approach, the clay content distribution constructs the permeability alteration model that is reflected in Gamma-Ray (GR) response, where GR horizontal changes within a layer found to be different from another layer. Therefore, a statistical GR analysis that searches for the optimum GR-Permeability model is developed. This permeability estimation approach is called Permeability Active Searching (PASZ).


Permeability is one of the most required input parameters to lot's of reservoir processes. These processes can be summarized as follows:

  1. Reservoir Developement

  2. Reservoir Simulation

  3. Reservoir Management

In applying these processes, good permeability data was always vital and it was the Reservoir Engineers aim to have permeability data that has the most of the following:

  1. Reliable

  2. Continuous

  3. Full coverage

  4. Handy

  5. Match production history

Historically however, it was very hard to reach these reservoir engineers wishes while the harder part was to be able to achieve it in carbonate.

By analyzing these five reservoir engineers' requirements, it was clear that the need to determine Permeability from Log data is essential. This is because logging data has the following features:

  1. Capture lots rock and fluid static properties like (Porosity, Water saturation, Lithology and Permeability).

  2. it is a consistent continuous measurement when the logging speed is constant across the reservoir and that how it should be as part of the logging operation procedure.

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