1887

Abstract

Summary

NMR relaxometry now is a powerful tool for detecting and distinguishing of reservoir fluids, such as free and bound water, oil. NMR data allows studying the types of fluids and their distribution in a deposit penetrated by the well. NMR can identify the intervals in which hydrocarbons are present and predict their recoverability. In this work, we aimed to optimize of calculating time for the integrals arising in the NMR forward and inversion problems. We propose Legendre polynomial expansion method for the modeling relaxation curves problem in the NMR relaxometry. This tool reduces significantly the computational complexity of the relaxation curve calculation, and hence it reduces the calculation time in comparison with numerical integration methods. Since we use an analytic expression for the integral, the calculation accuracy depends only on the integration error. The given approximation error is achieved due to the choice of the maximum degree of the polynomial at the stage of calculating the coefficients of the series of the Legendre polynomials.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201801848
2018-05-14
2024-04-26
Loading full text...

Full text loading...

References

  1. Borgia, G. C., Brown, R. J. S., FantazzininP.
    [1998]. Uniform-Penalty Inversion of Multiexponential Decay Data. Journal of magnetic resonance132, 65–77
    [Google Scholar]
  2. Coates, G.R., XiaoLizhi, Prammer, M.G.
    [2000]. NMR Logging. Principles & applications. Hulliburton Energy Services Publishing, Houston (USA).
    [Google Scholar]
  3. Farrer, T. C., Becker, E. D.
    [1971] Pulse and Fourier Transform NMR: Introduction to Theory and Methods. New York and London, Academic Press.
    [Google Scholar]
  4. GangYu, Zhizhan Wang, K. Mirotchnik, Lifa Li
    [2006]. Application of Magnetic Resonance Mud Logging for Rapid Reservoir Evaluation. Poster presentation at AAPG Annual Convention, Houston, Texas, April 9–12.
    [Google Scholar]
  5. Himmelblau, D.
    [1972]. Applied nonlinear programming. McGraw-Hill
    [Google Scholar]
  6. Mirotchnik, K., Kryuchkov, S., Strack, K.
    [2004]. A Novel Method to Determine NMR Petrophysical Parameters From Drill Cuttings. SPWLA 45th Annual Logging Symposium. Pare MM
    [Google Scholar]
  7. Muravyev, L.A., Zhakov, S.V.
    [2016]. Methodical issues of investigations with laboratory NMR relaxometer. Geoinformatics 2016: XVth International Conference on Geoinformatics - Theoretical and Applied Aspects. Ukraine. DOI: 10.3997/2214‑4609.201600527
    https://doi.org/10.3997/2214-4609.201600527 [Google Scholar]
  8. Lawson, C.L., Hanson, R.J.
    [1974]. Solving Least Squares Problems, Prentice-Hall.
    [Google Scholar]
  9. Provencher, S.W. [1982]. CONTIN: A general purpose constrained regularization program for inverting noisy linear algebraic and integral equations. Comput. Phys. Commun.27, 229.
    [Google Scholar]
  10. RafaelSalaezar-Tio, BoginSun
    [2010]. Monte Carlo optimazation-inversion methods for NMR. Petrophysics, vol.51, no.3, 208–218
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201801848
Loading
/content/papers/10.3997/2214-4609.201801848
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error