Abstract

In this paper, the low-permeability reservoir was subdivided into several units based on three models; in the first model, porosity, permeability, pore sizes, and shale volume were used as an input in the heterogeneous rock analysis clustering workflow to define rock units; in the second model, rock types were defined using flow zone index. The third flow unit discriminator was proposed by the author; the model is based on relation between porosity, permeability, irreducible water saturation, and pore size distribution. Also, Wyllie–Rose equation for permeability in tight reservoir was core-calibrated, and coefficients e, d, and Kw were established. The reservoir is built of thin layers of sandstones with variable porosity, permeability, pore sizes, and irreducible water. The research was performed in two wells where as input well log data, the laboratory results of mercury injection porosimetry, permeability measurements, and nuclear magnetic resonance data were used. Furthermore, it was investigated whether the presence of fractures identified on XRMI images were strictly related to one flow unit.

Highlights

  • Reservoir rock typing drives the quality of the distribution of petrophysical parameters in three (3D) Earth models and is crucial to reservoir characterization

  • This study presents rock typing with an understanding of petrophysical properties such as porosity, pore size, permeability, and capillary water content

  • Certain rock type can comprise entire range of porosity and permeability, which is not physically reliable. It is a great method for accurate core-calibrated permeability prediction. The another reason why flow zone index (FZI) method fails is the presence of fractures, and mismatch between calculated and measured porosity can be observed, and FZI calculated from measured porosity and permeability will be different from these calculated from well log data

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Summary

Introduction

Reservoir rock typing drives the quality of the distribution of petrophysical parameters in three (3D) Earth models and is crucial to reservoir characterization. Sensitivity decreases when there is an unusual relation between porosity–permeability or there is no relationship K–PHIE due to large amount of small disconnected pores This method could be used in each reservoir to obtain accurate permeability curve, which fits much better to core measurements (Fig. 12, track 2). In FZI classification, this unit is related to the presence of fractures where despite the low-porosity and low average values of pore sizes, and the measured permeability was high. The last very tight impermeable unit TRQI 4 has similar properties as TRQI 3, but it covers wide range of porosities from 0.4 to 5% It represents the intervals where despite the high porosities of 4–5%, permeability and pore sizes remain low, which is related to zones where part of pores are disconnected and has no impact on the permeability values

Conclusions
Findings
Compliance with ethical standards

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