A Method for Improving Permeability Accuracy of Tight Sandstone Gas Reservoirs Based on Core Data and NMR Logs

Liang Liu,Guoshu Huang,Heping Pan,Chengxiang Deng

doi:10.3390/en12152859

Liang Liu, Guoshu Huang + Show 2 more

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https://doi.org/10.3390/en12152859

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Journal: Energies	Publication Date: Jul 25, 2019
Citations: 1	License type: CC BY 4.0

Affiliation: China University of Geosciences

Abstract

Accurate calculation of the permeability of tight sandstone gas reservoirs has been a challenge, due to the enhanced effect of pore structure. Reservoir permeability with the same porosity and different pore structure often varies greatly. The permeability estimated by the traditional core sample regression analysis method has low accuracy, and the nuclear magnetic resonance (NMR) logging method is affected by the hydrocarbon of the reservoir. In this paper, the defined parameter can effectively quantify the difference of pore structure. Based on regression analysis of core measurement data, the model with optimal factor parameters of permeability calculation is established. This method combines the advantages of empirical models and pore structure models in calculating permeability. The results show that the method can effectively improve the accuracy of permeability. It has been successfully applied to the tight sandstone gas reservoir of He3 member in Hangjinqi area, Ordos Basin, China. Compared with other permeability theoretical models, it provides a more accurate and practical method for calculating permeability.

Highlights

IntroductionIt is related to porosity, and to other factors such as pore structure
Permeability is an important petrophysical property in reservoir evaluation [1]
The prediction method for permeability of tight sandstone gas reservoirs was studied. This method calculates the permeability using equations with parameters m, n, a, and b, which combine the advantages of core samples and nuclear magnetic resonance (NMR) logs

Summary

Introduction

It is related to porosity, and to other factors such as pore structure. Kozeny proposed a formula based on capillary theory [2]. Carman developed this formula [3], namely the Kozeny-Carman formula. Since the specific surface area cannot be directly obtained from logging data, the application of Kozeny-Carman formula is limited. The irreducible water saturation can reflect the change of the pore structure to a certain extent, but the premise needs to accurately calculate the irreducible water saturation. The pore connectivity is the main factor affecting permeability [5]. In order to predict reliable permeability, in addition to porosity, other parameters such as pore shape, pore size, and distribution must be considered [6]

Methods

Results

Discussion

Conclusion