Multiple permeability predictions using an observational learning algorithm

P.M. Wong; M. Jang; S. Cho; T.D. Gedeon

doi:10.1016/S0098-3004(00)00026-1

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Multiple permeability predictions using an observational learning algorithm

Journal article

Peer reviewed

Multiple permeability predictions using an observational learning algorithm

P.M. Wong, M. Jang, S. Cho and T.D. Gedeon

Computers & Geosciences, Vol.26(8), pp.907-913

2000

DOI: https://doi.org/10.1016/S0098-3004(00)00026-1

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Abstract

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.

Details

Title: Multiple permeability predictions using an observational learning algorithm
Authors/Creators: P.M. Wong (Author/Creator)
M. Jang (Author/Creator) - Pohang University of Science and Technology
S. Cho (Author/Creator) - Seoul National University
T.D. Gedeon (Author/Creator) - Murdoch University
Publication Details: Computers & Geosciences, Vol.26(8), pp.907-913
Publisher: Pergamon Press
Identifiers: 991005542300207891
Murdoch Affiliation: School of Information Technology
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 8 Earth Sciences; 8.140 Water Resources; 8.140.513 Reservoir Dynamics
Web Of Science research areas: Computer Science, Interdisciplinary Applications; Geosciences, Multidisciplinary
ESI research areas: Computer Science