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Neural Computing and Applications

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01.04.2012 | Review

Artificial neural networks workflow and its application in the petroleum industry

verfasst von: N. I. Al-Bulushi, P. R. King, M. J. Blunt, M. Kraaijveld

Erschienen in: Neural Computing and Applications | Ausgabe 3/2012

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Abstract

We develop a neural network workflow, which provides a systematic approach for tackling various problems in petroleum engineering. The workflow covers several design issues for constructing neural network models, especially in terms of developing the network structure. We apply the model to predict water saturation in an oilfield in Oman. Water saturation can be accurately obtained from data measured from cores removed from the oil field, but this information is limited to a few wells. Wireline log data are more abundantly available in most wells, and they provide valuable, but indirect, information about rock properties. A three-layered neural network model with five hidden neurons and a resilient back-propagation algorithm is found to be the best design for the saturation prediction. The input variables to the model are density, neutron, resistivity, and photo-electric wireline logs, and the model is trained using core water saturation. The model is able to predict the saturation directly from wireline logs with a correlation coefficient (r) of 0.91 and an error of 2.5 saturation units on the testing data.

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Titel: Artificial neural networks workflow and its application in the petroleum industry
verfasst von: N. I. Al-Bulushi
P. R. King
M. J. Blunt
M. Kraaijveld
Publikationsdatum: 01.04.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0501-6

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