1887

Abstract

Summary

Dolomite reservoirs host significant volume of world’s hydrocarbons. Understanding the porosity distribution within such strata is crucial for hydrocarbon exploration. In this study, 100 dolomite samples were collected from Saudi Arabia to investigate the main controlling factors on sonic velocity, and to examine the application of porosity prediction from acoustic impedance (P-impedance).

Collected samples were plugged for porosity and velocity measurements. Thin-section were prepared from each sample to define the texture and pore types. Acoustic impedances were calculated using compressional velocity and bulk density.

The petrographic analysis revealed that the studied samples could be separated into two main groups Fabric-preserving dolostones, and Non-fabric preserving dolostones. In addition, pore type is the main controlling factor on the sonic velocity and consequently on the calculated acoustic impedances. The porosity-impedance relation reveals that the two groups could be separated at porosity higher than 10%. Each group showed distinct porosity-impedance trend characterized by high correlation (R2> 0.94). The fabric-preserving dolostones show higher impedances than non-fabric preserving dolostones. This is mainly due to the dominance of stiffer pores within the fabric-preserving dolostones, compared to less stiff pores within the non-fabric preserving dolostones. The results of this study can enhance the porosity prediction in dolostone strata.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2022617012
2022-11-15
2024-04-29
Loading full text...

Full text loading...

References

  1. Dvorkin, J., Gutierrez, M. A., and Grana, D., 2014, Seismic reflections of rock properties, Cambridge University Press.
    [Google Scholar]
  2. Ehrenberg, S., Eberli, G., Keramati, M., and Moallemi, S., 2006, Porosity-permeability relationships in interlayered limestone-dolostone reservoirs: AAPG bulletin, v. 90, no. 1, p. 91–114.
    [Google Scholar]
  3. Janson, X., and Lucia, F. J., 2018, Matrix microcrystalline structure and acoustic properties of oomoldic dolograinstone: Geophysics, v. 83, no. 4, p. MR199–MR210.
    [Google Scholar]
  4. Mojeddifar, S., Kamali, G., and Ranjbar, H., 2015, Porosity prediction from seismic inversion of a similarity attribute based on a pseudo-forward equation (PFE): a case study from the North Sea Basin, Netherlands: Petroleum Science, v. 12, no. 3, p. 428–442.
    [Google Scholar]
  5. Omidpour, A., Mahboubi, A., Moussavi-Harami, R., and Rahimpour-Bonab, H., 2022, Effects of dolomitization on porosity – Permeability distribution in depositional sequences and its effects on reservoir quality, a case from Asmari Formation, SW Iran: Journal of Petroleum Science and Engineering, v. 208, p. 109348.
    [Google Scholar]
  6. Sun, S. Q., 1995, Dolomite reservoirs: porosity evolution and reservoir characteristics: AAPG bulletin, v. 79, no. 2, p. 186–204.
    [Google Scholar]
  7. Zhang, Z., Zhang, H., Li, J., and Cai, Z., 2021, Permeability and porosity prediction using logging data in a heterogeneous dolomite reservoir: An integrated approach: Journal of Natural Gas Science and Engineering, v. 86, p. 103743.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.2022617012
Loading
/content/papers/10.3997/2214-4609.2022617012
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