Integration Study of Reservoir Rock Typing and Reservoir Prediction in TM Oil Field

  • Rutai DuanEmail author
  • Leyuan Fan
  • Guiqin Han
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)


Reservoir rock typing and reservoir prediction are very important in reservoir characterization. Understanding the distribution of different types of reservoir plays an important role in successful prediction of reservoir performance. This paper focuses on the integration study of rock typing and reservoir prediction. A detailed workflow has been demonstrated through a case study for a highly heterogeneous reservoir in TM oil field. Three different reservoir types have been distinguished by their distinct storage and flow capacity characteristics summarized from mercury injection capillary pressure curves and combining rock-pore-throat size distribution curves. Acoustic impedance of these three different types of reservoirs has been analyzed, and their distribution characteristics have been predicted through geostatistical inversion calibrated by core and logging data. All this provide an effective and perspective geological reference for reservoir characterization in TM field. And it also can be used in other similar reservoirs.


Reservoir rock typing Mercury injection capillary pressure curve Pore-throat size distribution curve Reservoir prediction 


  1. 1.
    Gunter GW, Finneran JM, Hartmann DJ, Miller JD (1997) Early determination of reservoir flow units using an integrated petrophysical method. In: Paper SPE 38679 presented at the SPE annual technical conference and exhibition in San Antonio, Texas, 5–8 Oct 1997.
  2. 2.
    Gomes JS, Ribeiro MT, Strohmenger CJ, Negahban S, Kalam MZ (2008) Carbonate reservoir rock typing—the link between geology and SCAL. In: Paper SPE 118284, international petroleum exhibition and conference, Abu Dhabi, UAE, 3–6 Nov 2008Google Scholar
  3. 3.
    Rushing JA, Newsham KE, Blasingame TA (2008) Rock typing: keys to understanding productivity in tight gas sands. In: Paper SPE 114164, SPE unconventional reservoirs conference, Keystone, Colorado, 10–12 Feb 2008Google Scholar
  4. 4.
    Basioni M, Negahban S, Dawood A, Mahdi AE, Bahamaish J (2008) Reservoir rock typing from crest to flank. In: Paper SPE 117728, international petroleum exhibition and conference, Abu Dhabi, UAE, 3–6 Nov 2008Google Scholar
  5. 5.
    Chen Z, Zhu G (1997) Research progress on the method of seismic reservoir prediction. Prog Geophys 4:006Google Scholar
  6. 6.
    Sun S, Peng S (2007) Geostatistical inversion method and application in the prediction of thin reservoirs. J Xi’an Shiyou Univ Nat Sci Edit 22(1):41–44Google Scholar
  7. 7.
    Bian S, Di B, Dong Y et al (2011) Application of geostatistical inversion in reservoir prediction in the third member of Shahejie Formation, Baimiao Gas-field, Dongpu Depression. Oil Geophys Prospect 45(3):399–405Google Scholar
  8. 8.
    Torres VC, Victoria M (1999) Trace-based and geostatistical inversion of 3D seismic data for thin-sand delineation: an application in San Jorge Basin, Argentina. Lead Edge 18(9):1070–1077CrossRefGoogle Scholar
  9. 9.
    Carlos TV, Raghu K, Chunduru A (2003) Integrated interpretation of wireline and 3d seismic data to delineate thin oil producing sands in San Jorge Basin. SPE 87304Google Scholar
  10. 10.
    Li F, Ji Z, Zhao G et al (2007) Methodology and application of stochastic seismic inversion. Pet Explor Dev 34(4):451–455Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Geoscience CentreGreat Wall Drilling Company, CNPCBeijingChina

Personalised recommendations