Estimation of permeability in carbonates has been a challenge for many years. Well logs, particularly high-resolution logs, are influenced by rock properties. Therefore, when there is limited core coverage and scarce high-resolution log data, permeability estimation using the standard suite of logs (resistivity, density, neutron, caliper, gamma ray) is crucial for populating and constraining a 3D geological permeability model.

Two new traces, the deep and micro resistivity activity traces, are derived from the corresponding resistivity logs. The activity traces are not affected by fluid effects and, thus, preserve better the formation characteristics. Permeability estimation using an artificial neural network approach is made through a two-step process. In the first step, probabilities of log-derived rock types are estimated from a trained neural network using the micro and deep resistivity activity traces, and the standard suite of logs as input. In the second step, a separately trained neural network uses rock type probabilities from step 1, along with a suite of logs to predict permeability.

Two examples are provided to illustrate the validity of the method in predicting permeability in a heterogeneous carbonate reservoir located in Abu Dhabi, UAE. This reservoir exhibits permeability ranging from half a milli-Darcy to more than 20 Darcies. The first example represents a blind test where the estimated permeability shows good agreement with core permeability data. The second example demonstrates the predictive capability of the method in a non-cored well that is located in the vicinity of cored wells. The estimation technique is robust and was found valuable to supplement core data in the construction of geo-cellular permeability models.

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