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Petrophysical Data Analytics for Reservoir Characterization

  • Y. Z. Ma
Chapter

Abstract

This chapter presents an overview of petrophysical analysis, mainly from the viewpoint of data analytics. Petrophysical analysis is critical in a reservoir study because it provides a primary source of input data for integrated reservoir characterization and resource evaluation. Wireline logging provides various recordings of subsurface formation properties and well logs are the main sources for petrophysical analysis. Logging records are first used for single-well evaluations and then extended to fieldwide resource evaluation and reservoir modeling.

Logging technology has grown exponentially since the first electrical log was recorded in 1927. Modern log suites include gamma ray (GR), spontaneous potential (SP), density, neutron, sonic, nuclear magnetic resonance (NMR), photoelectric factor (PEF), and various resistivity logs. These data are used to evaluate rock properties, including porosity, fluid saturation, permeability, mineral compositions, and lithofacies (see Appendix 9.1).

References

  1. Amaefule, J. O., M. Altunbay, D. Tiab, D.G. Kersey, and D.K. Keelan, 1993, Enhanced reservoir description: Using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells: SPE 26436, SPE Annual Technical Conference and Exhibition, Houston, Texas.Google Scholar
  2. Archie, G. E. (1950). Introduction to petrophysics of reservoir rocks. AAPG Bulletin, 34, 943–961.Google Scholar
  3. Bhuyan K., & Passey, Q. R. (1994). Clay estimation from GR and neutron-density porosity logs. Presented at the SPWLA 35th Annual Logging symposium.Google Scholar
  4. Cao, R., Wang, Y., Cheng, L., Ma, Y. Z., Tian, X., & An, N. (2016). A new model for determining the effective permeability of tight formation. Transport in Porous Media, 112, 21–37.CrossRefGoogle Scholar
  5. Cosentino, L. (2001). Integrated reservoir studies. Paris: Editions Technip.Google Scholar
  6. Crain, E. R. (1986). The log analyst handbook (700 p). Tulsa: PennWell Books.Google Scholar
  7. Dewan, J. T. (1983). Essentials of modern open-hole log interpretation (361p). Tulsa: PennWell Books.Google Scholar
  8. Herron, M. M., & Matteson, A. (1993). Elemental composition and nuclear parameters of some common sedimentary minerals. Nuclear Geophysics, 7(3), 383–406.Google Scholar
  9. Holditch, S. A. (2006). Tight gas sands. Journal of Petroleum Technology, 58, 86–93.CrossRefGoogle Scholar
  10. Jennings, J. W. (1999). How much core-sample variance should a well-log model reproduce? SPE Reservoir Evaluation & Engineering, 2(5), 442–450.CrossRefGoogle Scholar
  11. Kennedy, M. (2015). Practical petrophysics. Amsterdam: Elsevier.Google Scholar
  12. Kleinberg, R. L., & Vinegar, H. J. (1996). NMR properties of reservoir fluids. Society of Petrophysicists and Well-Log Analysts.Google Scholar
  13. Lucia, J. F. (2007). Carbonate reservoir characterization (2nd ed.). Berlin: Springer.Google Scholar
  14. Ma, Y. Z. (2015). Unconventional resources from exploration to production. In Y. Z. Ma & S. A. Holditch (Eds.), Unconventional oil and gas resource handbook – Evaluation and development (pp. 3–52). Waltham: Elsevier,. ISBN 978-0-12-802238-2.Google Scholar
  15. Ma, Y. Z., & Gomez, E. (2015). Uses and abuses in applying neural networks for predicting reservoir properties. Journal of Petroleum Science and Engineering.  https://doi.org/10.1016/j.petrol.2015.05.006.CrossRefGoogle Scholar
  16. Ma, Y. Z., Seto, A., & Gomez, E. (2008). Frequentist meets spatialist: A marriage made in reservoir characterization and modeling: SPE 115836, SPE ATCE. Denver.Google Scholar
  17. Ma Y. Z. et al. (2014). Identifying hydrocarbon zones in unconventional formations by discerning Simpson’s Paradox. Paper SPE 169495 presented at the SPE Western North America and Rocky Mountain Joint Conference, 17–18 April, Denver, Colorado, USA.Google Scholar
  18. McCarty, D. K., Theologou, P. N., Fischer, T. B., Derkowski, A., Stokes, R., & Ollila, A. (2015). Mineral-chemistry quantification and petrophysical calibration for multimineral evaluations: A nonlinear approach. AAPG Bulletin, 99(7), 1371–1397.CrossRefGoogle Scholar
  19. Moore, W. R., Ma, Y. Z., Urdea, J., & Bratton, T. (2011). Uncertainty analysis in well log and petrophysical interpretations. In Y. Z. Ma & P. LaPointe (Eds.), Uncertainty analysis and reservoir modeling (AAPG Memoir 96). Tulsa.Google Scholar
  20. Moore, W. R., Ma, Y. Z., Pirie, I., & Zhang, Y. (2015). Tight gas sandstone reservoirs – Part 2: Petrophysical analysis and reservoir modeling. In Y. Z. Ma, S. Holditch, & J. J. Royer (Eds.), Handbook of unconventional resource. Amsterdam: Elsevier.Google Scholar
  21. Nelson, P. N. (1994). Permeability-porosity relationships in sedimentary rocks. The Log Analyst, 35, 33–62.Google Scholar
  22. Peters, E. J. (2012). Advanced petrophysics. 3 volumes. Austin: Live Oak Book Company.Google Scholar
  23. Pitman, E. D. (1992). Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone. AAPG Bulletin, 76(2), 191–198.Google Scholar
  24. Prensky, S. E. (1984). A Gamma-ray log anomaly associated with the Cretaceous-Tertiary boundary in the Northern Green River basin, Wyoming, USGS Open-file 84-753, edited by BE Law.Google Scholar
  25. Schlumberger. (1999). Log interpretation principles/applications, 8th print. Sugarland, TX: Schlumberger Educational Services.Google Scholar
  26. Shepherd, R. G. (1989). Correlations of permeability and grain size. Groundwater, 27(5), 633–638.CrossRefGoogle Scholar
  27. SPWLA. (1982). Shaly Sand Reprint Volume, July.Google Scholar
  28. Steiber, S. J. (1970). Pulsed neutron capture log evaluation in the Louisiana Gulf Coast. Paper presented at the Fall Meeting of the Society of Petroleum Engineers of AIME, 4-7 October, Houston, Texas, USA. SPE-2961-MS.Google Scholar
  29. Tiab, D., & Donaldson, E. C. (2012). Petrophysics (3rd ed.). Waltham: Gulf Professional Pub.Google Scholar
  30. Worthington, P. E. (1985). The evolution of Shaly-sand concepts in reservoir evaluation. The Log Analyst, 26, 23–40.Google Scholar
  31. Wu, T., & Berg, R. R. (2003). Relationship of reservoir properties for Shaly sandstones based on effective porosity. Petrophysics, 44, 328–341.Google Scholar
  32. Zeybek, A. D., Onur, M., Tureyen, O. I., Ma, S. M., Shahri, A. M., & Kuchuk, F. J. (2009). Assessment of uncertainty in saturation estimated from Archie’s equation. Society of Petroleum Engineers.  https://doi.org/10.2118/120517-MS.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Y. Z. Ma
    • 1
  1. 1.SchlumbergerDenverUSA

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