Neural networks and the bias/variance dilemma
Neural Computation
Multiple permeability predictions using an observational learning algorithm
Computers & Geosciences - Special issue on applications of virtual intelligence to petroleum engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Petrophysical data prediction from seismic attributes using committee fuzzy inference system
Computers & Geosciences
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Integration of fuzzy systems and genetic algorithm in permeability prediction
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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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.