4.4 Article

Numerical simulation and analysis for low-frequency rock physics measurements

Journal

JOURNAL OF GEOPHYSICS AND ENGINEERING
Volume 14, Issue 5, Pages 1203-1214

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/1742-2140/aa7300

Keywords

numerical simulation; rock physics; finite difference; differential acoustic resonance spectroscopy; stress-strain

Funding

  1. National Basic Research Program of China [2013CB228600]
  2. National Natural Science Fund of China [41404105]

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In recent years, several experimental methods have been introduced to measure the elastic parameters of rocks in the relatively low-frequency range, such as differential acoustic resonance spectroscopy (DARS) and stress-strain measurement. It is necessary to verify the validity and feasibility of the applied measurement method and to quantify the sources and levels of measurement error. Relying solely on the laboratory measurements, however, we cannot evaluate the complete wavefield variation in the apparatus. Numerical simulations of elastic wave propagation, on the other hand, are used to model the wavefield distribution and physical processes in the measurement systems, and to verify the measurement theory and analyze the measurement results. In this paper we provide a numerical simulation method to investigate the acoustic waveform response of the DARS system and the quasi-static responses of the stress-strain system, both of which use axisymmetric apparatus. We applied this method to parameterize the properties of the rock samples, the sample locations and the sensor (hydrophone and strain gauges) locations and simulate the measurement results, i.e. resonance frequencies and axial and radial strains on the sample surface, from the modeled wavefield following the physical experiments. Rock physical parameters were estimated by inversion or direct processing of these data, and showed a perfect match with the true values, thus verifying the validity of the experimental measurements. Error analysis was also conducted for the DARS system with 18 numerical samples, and the sources and levels of error are discussed. In particular, we propose an inversion method for estimating both density and compressibility of these samples. The modeled results also showed fairly good agreement with the real experiment results, justifying the effectiveness and feasibility of our modeling method.

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