Abstract
The article considers the effect of porous media on elastic wave field. Based on numerical modeling, diffraction pattern of the wave propagating through a single pore in carbonates has been produced. Matrix properties (calcite and dolomite) and fluid (water) are modeled based on thin core section image. The qualitative comparison with the available computational data has been performed. Provided that ensemble of pores is involved, the effect of porous medium on seismic field has been studied. For comparison with experimental data the model of porous sintered aluminum Al-6061 has been considered. The processing of numerical modeling results made it possible to estimate average velocities in the model of porous aluminum and compare them with physical modeling data. The provided estimates have indicated qualitative (single pore) and quantitative (ensemble of pores) correlation of simulation and experiment results.
Highlights
Porous media and their effect on the elastic wave field has been a current issue for the past few decades
This interest is due to both material engineering objectives and hydrocarbon exploration. These two different knowledge domains are interrelated in physical modeling
In laboratory modeling of seismic waves in porous media several types of solids including aluminum [1, 2], soda-lime glass [3] can be used as a matrix material
Summary
Porous media and their effect on the elastic wave field has been a current issue for the past few decades This interest is due to both material engineering objectives and hydrocarbon exploration. In laboratory modeling of seismic waves in porous media several types of solids including aluminum [1, 2], soda-lime glass [3] can be used as a matrix material In laboratory experiments both statistical and dynamic characteristics are estimated, P- and S- wave velocities being basic types among them. The analysis of numerical results in wave field calculation in porous media implies estimation of the wave velocity change The comparison of these estimates with physical modeling data is referred to as quantitative calibration.
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