Permeability is an important reservoir evaluation property and is vital to well completion strategies, reservoir productivity, 3-D geocellular model construction, and reservoir simulation. Despite its importance, it is still one of the most difficult petrophysical properties to predict accurately. Simple porosity-permeability relationships have been used extensively, even though the relationship between them is admissibly poor. The problem is even more complex when conventional open-hole log responses are used to predict permeability in heterogeneous carbonates. Logs are obtained in all wells, whereas only a few wells are cored. Therefore, it becomes obvious that if conventional logs can be used to predict permeability, then continuous permeability traces can be as commonplace as porosity traces in well sets, instead of sparse core data.
This paper describes the use of fuzzy logic to model and predict permeability in cored wells by calibrating core permeability against conventional open-hole logs. Fuzzy logic is an application of recognized statistical techniques. It is an extension of conventional Boolean logic (zeros and ones) that has been developed to handle the concept of "partial truth" and for modeling uncertainties. The permeability models developed were then used to generate permeability trace in each well across the field. In this application, fuzzy logic was preferred to the conventional porosity-permeability regression and neural networks because the results worked best as demonstrated by blind verification to core data and the excellent quality of reservoir simulation results.
There was a very good match between the modeled permeability, the core data, and flow meter data in cored wells that had flow meter data. The match was also very good in non-cored wells that had pressure build-up data. Having validated the model in these wells with hard data, we proceeded to populate the 3-D geocellular model with the generated permeability traces in all wells.