Modeling acoustic attenuation of discrete stochastic fractured media View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Guiwu Chen, Lei Song, Ray Ruichong Zhang

ABSTRACT

The acoustic response has many important roles in seismic exploration and nondestructive testing. It enables the development of fracture classification and sizing. In this paper, we combined Hudson’s effective medium scheme and finite-difference time-domain modeling method to simulate acoustic wave propagation in fractured media. Fractures are represented by discrete fracture networks, allowing for a state-of-the-art representation of natural fracture networks by a negative Exponential Law length distribution. The propagation of acoustic waves that are emitted by a point source and reflected from a fractured area in a 2D digital rock model are examined numerically with the purpose of developing an acoustic inference of fracture properties. In these fractured models, we vary the number and mean length of fractures to explore the relation between internal structure of rock and acoustic wave field characters. The modeling results indicate that acoustic wave field is more sensitive to the fracture number than to the mean of the fracture length. Moreover, a fracture-dependent attenuation analysis of the reflection records of discrete stochastic fractured models is obtained. The frequency- and time- dependent attenuation profiles feature two parts in frequency, (1) fracture-to-background at lower frequencies and (2) fracture-to-fracture at higher frequencies. Our results indicate that accounting for attenuation effects may not only allow for improving estimation of fracture number, but also provide information about geometrical characteristics of length distribution. Such an approach can be used to estimate nature fracture network properties with given acoustic records. More... »

PAGES

1-12

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40328-018-0237-9

DOI

http://dx.doi.org/10.1007/s40328-018-0237-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1109824838


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