• DocumentCode
    2523974
  • Title

    Analysis of the method of Black box modeling of drill string dynamics by least squares method

  • Author

    Majeed, Fesmi Abdul ; Magid, Youssef Lotfy Abdel ; Karki, Hamad ; Karkoub, Mansour

  • Author_Institution
    Mech. Eng. Dept., Pet. Inst., Abu Dhabi, United Arab Emirates
  • fYear
    2010
  • fDate
    10-12 Sept. 2010
  • Firstpage
    257
  • Lastpage
    261
  • Abstract
    The high dependency on oil and gas, leads the exploration and efficiency of the drilling process to be very demanding. Most of the tests conducted to study drill string nonlinearities and failures are performed by simulations on models derived for the purpose. Hence, mathematical modeling of a process is usually the first step taken to understand and analyze the dynamics of any process. Most of the mathematical models of the small scale experimental set ups of drilling rigs are developed by analytical modeling. This paper intends to project the use of Black box modelling procedure as a better, simpler and accurate alternate to analytical modeling. An auto regressive moving average exogenous (ARMAX) model is designed for the drill string experimental set up. The method of converging to the selected model using correlation tests and analyzing the prediction error graphs are discussed in the paper in detail. The least squares method provides unbiased estimates of the process model coefficients. The attraction of the method lies in the fact that the nonlinearities exhibited by the process will also be included in the model, without increasing the complexity or a change in the method used to converge to obtain the model.
  • Keywords
    autoregressive moving average processes; drilling; failure analysis; graph theory; least squares approximations; oil drilling; ARMAX model; black box modeling; correlation test; drill string dynamics; drill string failure; drill string nonlinearity; drilling process efficiency; drilling rig; exploration; gas dependency; least squares method; mathematical modeling; oil dependency; prediction error graph; process model coefficient; regressive moving average exogenous model; Analytical models; Data models; Mechanical engineering; ARMAX model; drillstring; system identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechanical and Electrical Technology (ICMET), 2010 2nd International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-8100-2
  • Electronic_ISBN
    978-1-4244-8102-6
  • Type

    conf

  • DOI
    10.1109/ICMET.2010.5598362
  • Filename
    5598362