SPE Reservoir Evaluation & Engineering
Volume 12, Number 6, December 2009, pp. 921-928

SPE-114306-PA

Implicit Approximation of Neural Network and Applications

  • Dao-lun Li, University of Science and Technology of China
  • De-tang Lu, University of Science and Technology of China
  • Wen-shu Zha, University of Science and Technology of China

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DOI  More information 10.2118/114306-PA http://dx.doi.org/10.2118/114306-PA

Citation

  • Li, D., Lu, D., and Zha, W. 2009. Implicit Approximation of Neural Network and Applications. SPE Res Eval & Eng  12 (6): 921-928. SPE-114306-PA. doi: 10.2118/114306-PA.

Discipline Categories

  • 3 Management and Information
  • 3.5.2 Data Integration
  • 3.5.1 Knowledge Management

Summary

It is common practice that one of the reservoir properties is recognized as a complex function of several interrelated factors in neural-network applications used in oil-reservoir studies. Few methods are based on one reservoir property being recognized as a function of time and the spatial locations, which means that the reservoir-property data can be described as a time vector series. It is a great challenge for the artificial neural network to describe a time vector series because the neural network is unable to approximate a multivariate vector function effectively. By combining the principle of implicit curves and surfaces with the neural network, we present a novel way to process the time vector series. The method includes the following steps: mapping data, constructing an explicit function, training the neural network, extracting the isoline, and inverse mapping.

This paper presents an application to predict the isotope-logging data in 2002 with the known data from 2001 and 2003.

© 2009. Society of Petroleum Engineers

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History

  • Original manuscript received: 12 November 2007
  • Revised manuscript received: 13 January 2009
  • Manuscript approved: 22 May 2009
  • Published online: 29 September 2009
  • Version of record: 31 December 2009