Prediction model of permeability from well logs using Type-2 Fuzzy Logic Systems

In this paper, the viability and capability of using Type-2 Fuzzy Logic Systems as a novel approach for predicting permeability from Well Logs has been investigated and implemented. Type-2 fuzzy logic is powerful in handling uncertainties, including uncertainties in measurements and data used to calibrate the parameters. In the formulation used, the value of a membership function corresponding to a particular permeability value is no longer a crisp value; rather, it is associated with a range of values that can be characterized by a function that reflects the level of uncertainty. In this way, the model will be able to adequately account for all forms of uncertainties associated with predicting permeability from well log data, where uncertainties are very high and the need for stable results are highly desirable. Comparative studies have been carried out to compare the performance of the proposed framework with those earlier used methods, using real industrial reservoir data. Empirical results from simulation show that Type-2 FLS approach outperforms others in general and particularly in the area of stability and ability to handle data in uncertain situations, which are the common characteristics of well logs data. Another unique advantage of the newly proposed model is its ability to generate, in addition to the normal target forecast, prediction intervals as its by-products without extra computational cost.