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Multiple permeability predictions using an observational learning algorithm
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DOI：10.1016/S0098-3004(00)00026-1.png)
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Abstract
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Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs, Well log data are frequently available to infer this parameter along drilled wells. Many fundamental problems remain unsolved by most predictive models. This paper introduces the use of an improved neural network trained by an observational learning algorithm to provide solutions for two particular problems: the generation of additional or virtual samples when the number of training data is insufficient; and the generation of multiple permeability values at the same reservoir depth for reliability analyses. The methodology is illustrated by a case study in western Australia. Four drilled wells with well logs and core permeability are used in this study. The data from the first two wells are used for training, while the others are used as unseen data to test the performance of the model. The results show that the proposed method gives smaller error compared to multiple linear regression and other neural networks (simple committee networks and bootstrap aggregating). It also provides valuable information on the reliability of the permeability predictions which is consistent with the geological studies. (C) 2000 Elsevier Science Ltd. All rights reserved.
Keywords:
permeability
well logs
neural networks
observational learning
bootstrapping
virtual samples
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