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Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network

Part of the Studies in Computational Intelligence book series (SCI,volume 591)

Abstract

Reservoir simulation provides information about the behaviour of a reservoir in various production and injection conditions. Reservoir simulator is used to predict the future behaviour and performance of a reservoir field. However, the heterogeneity of reservoir and uncertainty in the reservoir field cause some obstacles in selecting the best calculation of oil, water and gas components that lead to the production system in oil and gas. This paper presents a dynamic well Surrogate Reservoir Model (SRM) to predict reservoir bottom-hole flowing pressure by varying the production rate constraint of a well. The proposed SRM adopted Radial Basis Neural Network to predict the bottom-hole flowing pressure of well based on the output data extracted from a numerical simulation model in a considerable amount of time with production constraint values. It is found that the dynamic SRM is capable to generate the promising results in a shorter time as compared to the conventional reservoir model.

Keywords

  • Radial Basis Function
  • Hide Neuron
  • Mean Absolute Percentage Error
  • Production Well
  • Grid Block

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-14654-6_17
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Acknowledgment

The authors also like to thank Universiti Teknologi PETRONAS for sponsoring the project funding under YUTP-EOR MOR.

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Correspondence to Suet-Peng Yong .

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Memon, P.Q., Yong, SP., Pao, W., Pau, J.S. (2015). Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-14654-6_17

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