Reservoir classification method based on nuclear magnetic resonance logging

Abstract

The invention discloses a reservoir classification method based on nuclear magnetic resonance logging, comprising the steps of: obtaining a nuclear magnetic resonance transverse relaxation time T2 spectrum of a depth point to be classified of a reservoir to be classified; calculating the nuclear magnetic resonance porosity of the depth point to be classified of a reservoir to be classified in dependence on the nuclear magnetic resonance T2 spectrum; employing a doublet Gaussian density function to fit the nuclear magnetic resonance T2 spectrum to obtain parameters representing pore structure characteristics of the depth point to be classified of a reservoir to be classified; employing a cluster analysis method to clarify the depth point to be classified of a reservoir to be classified in dependence on the nuclear magnetic resonance porosity of the depth point to be classified of a reservoir to be classified and the parameters representing pore structure characteristics of the depth point to be classified of a reservoir to be classified; and determining the reservoir type of the reservoir to be classified in dependence on the classification result of the depth point to be classified of a reservoir to be classified. The technical scheme provides strong technical support for accurate divide and reasonable development of a reservoir type.

Description

technical field [0001] The invention relates to the technical field of well logging evaluation in oil and gas exploration, in particular to a reservoir classification method based on nuclear magnetic resonance logging. Background technique [0002] Tight reservoirs have poor physical properties, strong heterogeneity, and complex pore structure. The reservoir classification method based on the experimental analysis and test results of coring samples cannot meet the needs of tight reservoir classification. NMR logging can provide continuous reservoir pore size distribution with logging depth, and extracting reservoir pore structure parameters through NMR logging T2 spectrum is the development direction of reservoir classification. [0003] At present, the method of extracting reservoir pore structure parameters by using NMR logging T2 spectrum is to convert NMR T2 spectrum into pseudo-capillary pressure curve. Take coring, and test the capillary pressure curve and nuclear mag...

AI Technical Summary

Problems solved by technology

The problem with this method is that it is impossible to establish an accurate conversion model when there are few cores or the core samples are not subjected to mercury injection experiments and NMR experiments at the same time; in addition, even if a better conversion model is obtained through core experiments, the model When applied to uncored well intervals, these transformation models are not suitable due to the heterogeneity of the reservoir, resulting in unsatisfactory reservoir classification results

[0004] At present, some people in foreign countries use bimodal Gaussian density function to fit the core capillary pressure curve, obtain parameters closely related to rock pore structure, and use these parameters to classify rocks, but this method has not been used to fit NMR logging T2 spectrum and research on reservoir classification

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