Effect of modeling porous media on the response of gamma-gamma well-logging tool

Author:

Rasouli Fatemeh S.,Masoudi S. Farhad

Abstract

AbstractThe well logging is known as a technique of making petrophysical measurements in the sub-surface earth formations through a drilled borehole to reach the characterization of the physical properties of rocks and fluids. Considering the fact that reservoirs are complex fractured media which the fluid can flow through the porosities, the distribution model of oil in the medium needs to be investigated in detail and to be well quantified. To study this effect, a typical gamma-gamma logging tool containing 137Cs source and two NaI detectors was modeled by using the MCNPX code. The medium was filled with numerous matrix-shaped blocks, each including rectangular cubes for modeling the oil flow in the formation. For an arbitrary set of oil concentrations and various formation materials, the response of the detectors for this model was studied. Taking into account the results corresponding to the traditional homogeneous mixture model for the formation, it was found that the deviations between the count rates for two models reach to about 10% and 22% for short spacing and far spacing detectors, respectively. The results also show that the slopes of the straight-line fits to the count rates, which is important for the evaluation of the density, deviate between about 73.3% and 53.8% for two simulated models. Investigating the effect of the presence of the drilling fluid on the count rate of the proposed model showed that for a given thickness of mudcake and the formation density, both detectors show approximately the same percentage of change in counting rate. However, these counts for the proposed model deviate from those of the mixture model between 5.1% and 28%. It can be concluded that defining a model for describing heterogeneities of a natural porous medium can effectively account for the prediction of density measurement in logging tools.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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