1887

Abstract

Summary

Nuclear magnetic resonance (NMR) is one of the most reliable techniques in obtaining permeability and porosity information of hydrocarbon reservoirs in today’s oil and gas industry. However, this technology is considerably expensive and its associated costs are the major limitations of its usage. Moreover, it is not possible to run NMR log in producing cased wells. Accordingly, the current research tries to suggest a novel alternative technique for the wide scale field measurements of the NMR log and tries to provide a more cost-effective and time-effective approach. Our introduced novel technique which is based on mathematical algorithms proved to act successfully in accomplishing desired purposes.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201700926
2017-06-12
2024-04-25
Loading full text...

Full text loading...

References

  1. Coates, G.R., Xiao, L., Prammer, M.G.
    , 1999. NMR Logging Principles and Applications. Halliburton Energy Services Publication, Houston.
    [Google Scholar]
  2. Golsanami, N., Sun, J., Zhang, Z.
    , 2016a. A review on the applications of the nuclear magnetic resonance (NMR) technology for investigating fractures. J. Appl. Geophys. 133, 30–38. doi: 10.1016/j.jappgeo.2016.07.026
    https://doi.org/10.1016/j.jappgeo.2016.07.026 [Google Scholar]
  3. , 2016b. A review on the applications of the nuclear magnetic resonance (NMR) technology for investigating fractures. J. Appl. Geophys. Elsevier B.V. doi: 10.1016/j.jappgeo.2016.07.026
    https://doi.org/10.1016/j.jappgeo.2016.07.026 [Google Scholar]
  4. Jang, J.-S.R.
    , 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man. Cybern. 23, 665–685. doi: 10.1109/21.256541
    https://doi.org/10.1109/21.256541 [Google Scholar]
  5. MATLAB
    , 2011a. Statistics Toolbox™ User’s Guide. The MathWorks, Inc.
    [Google Scholar]
  6. , 2011b. Neural Network Toolbox™ User’s Guide. The MathWorks, Inc.
    [Google Scholar]
  7. , 2011c. Fuzzy Logic Toolbox™ User’s Guide. The MathWorks, Inc.
    [Google Scholar]
  8. Sugiyama, M.
    , 2016. Chapter 30 — Ensemble Learning, in: Introduction to Statistical Machine Learning. pp. 343–354. doi: 10.1016/B978‑0‑12‑802121‑7.00041‑8
    https://doi.org/10.1016/B978-0-12-802121-7.00041-8 [Google Scholar]
  9. Toumelin, E., Sun, B.
    , 2009. Optimization of wireline nmr pulse sequences52, 1–15.
    [Google Scholar]
  10. Zadeh, L.A.
    , 1965. Fuzzy sets. Inf. Control8, 338–353. doi: 10.1016/S0019‑9958(65)90241‑X
    https://doi.org/10.1016/S0019-9958(65)90241-X [Google Scholar]
  11. Zhenguang, L., Lianjin, Z.
    , 1990. Application of relaxation time T1of ’H NMR spin lattice as organic maturation index. Exp. Pet. Geol. (in Chinese)12, 30–35.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201700926
Loading
/content/papers/10.3997/2214-4609.201700926
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