IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i10p989-d277956.html
   My bibliography  Save this article

A New Method for De-Noising of Well Test Pressure Data Base on Legendre Approximation

Author

Listed:
  • Fengbo Zhang

    (Zhanjiang Branch of CNOOC Ltd., Zhanjiang 524000, China
    These authors contributed equally to this work.)

  • Yuandan Zheng

    (Faculty of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China
    These authors contributed equally to this work.)

  • Zhenyu Zhao

    (School of Mathematics and Statistics, Shandong University of Technology, Zibo 255049, China
    Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524088, China)

  • Zhi Li

    (Faculty of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China)

Abstract

In this paper, noise removing of the well test data is considered. We use the Legendre expansion to approximate well test data and a truncated strategy has been employed to reduce noise. The parameter of the truncation will be chosen by a discrepancy principle and a corresponding convergence result has been obtained. The theoretical analysis shows that a well numerical approximation can be obtained by the new method. Moreover, we can directly obtain the stable numerical derivatives of the pressure data in this method. Finally, we give some numerical tests to show the effectiveness of the method.

Suggested Citation

  • Fengbo Zhang & Yuandan Zheng & Zhenyu Zhao & Zhi Li, 2019. "A New Method for De-Noising of Well Test Pressure Data Base on Legendre Approximation," Mathematics, MDPI, vol. 7(10), pages 1-10, October.
  • Handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:989-:d:277956
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/10/989/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/7/10/989/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:989-:d:277956. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.