There was always the challenge to match Permeability estimated from logs to Permeability derived from well tests. The main reason for this is the difference in scale, both vertically in the borehole (net contributing sands) as well as radially out into the formation. The well test that takes into consideration many hours of fluid flow into the borehole was always deemed more representative than permeability derived from logs that measures a smaller area and volume. However, this perception should not relegate log-derived permeability to an insignificant parameter within dynamic models. When wells are stimulated prior to well test (e.g. under-balanced perforation or chemical stimulation for clean-up), it is expected that the permeability from the well test be enhanced as seen in Iago-2 and Gorgon-3 wells.
This paper takes core data from wells in the Carnarvon Basin, create log relationships to predict permeability that match these core data, and compare these to wireline formation tester mobility and to well tests. The results are very promising, and the workflow proposed can be applied to any well in the basin. One of the objectives of this paper is to create a workflow that can be replicated easily and to use raw logs that are available across all wells, in order to reduce the uncertainty in the predicted permeability.
The reservoir sands of the Mungaroo formation are easily recognised by the cross over in the neutron-density logs, in both gas zones and water zones. This is the criteria used by operators to obtain formation pressure tests (MDT/RFT) and this is the same criteria used in this paper to define reservoir sands. Only those core data acquired in reservoir sands are used as the "Learning" dataset to predict permeability. Several learning datasets were created, and these were blind tested on other wells that were not part of the learning dataset. The results of these predicted permeabilities were cross plotted against core permeability that have been over-burden corrected and depth shifted to wireline logs. Where the match is not satisfactory, new learning datasets are derived and this step of the workflow is repeated. At the end, there are four groups of learning datasets that are used as the final results.
These four groups of datasets are associated with four sets of equations and these provides a very good match between the predicted log-derived permeabilities and core plug permeabilities. When compared to mobilities from formation pressure testers, the predicted permeabilities are a very good match. These were then compared to well test permeabilities showing an overall good match. This gives confidence that the predicted permeabilities from the four sets of equations are good and can be applied to any new well targeting the Mungaroo Formation in the Carnarvon Basin.