A committee machine with empirical formulas for permeability prediction

This study integrates log-derived empirical formulas and the concept of the committee machine to develop an improved model for predicting permeability. A set of three empirical formulas, such as the Wyllie–Rose, Coates–Dumanoir, and porosity models to correlate reservoir well-logging information with measured core permeability, are used as expert members in a committee machine. A committee machine, a new type of neural network, has a parallel architecture that fuses knowledge by combining the individual outputs of its experts to arrive at an overall output. In this study, an ensemble-based committee machine with empirical formulas (CMEF) is used. This machine combines three individual formulas, each of which performs the same evaluation task. The overall output of each ensemble member is then computed according to the coefficients (weights) of the ensemble averaging method that reflects the contribution of each formula. The optimal combination of weights for prediction is also investigated using a genetic algorithm.We illustrate the method using a case study. Eighty-two data sets composed of well log data and core data were clustered into 41 training sets to construct the model and 41 testing sets to validate the model's predictive ability. A comparison of prediction results from the CMEF model and from three individual empirical formulas showed that the proposed CMEF model for permeability prediction provided the best generalization and performance for validation. This indicated that the CMEF model was more accurate than any one of the individual empirical formulas performing alone.