Abstract

The knocking ball test is the in-situ handy test developed by the authors to measure deformation characteristics of rocks and rock masses quickly. The principle of this test is based on the Hertz theory which is the classical contact mechanics but still the suitable solution for non-adhesive contact problems of two elastic bodies. Therefore, the practical effectiveness of this novel test has been examined.

First, this paper shows the correlation between the elastic moduli E50obtained by uniaxial compression tests and the elastic moduli Ekb acquired by the knocking ball tests for 29 specimensconsisting of 8 rock types in order to confirm the accuracy of the measurement results of the knocking ball tests. As a result, it is found that multiplying Ekb by the correlation coefficient of 1.65, which corrects for the influence of the rough surface condition, gives the E50.

Second, the other kinds of the deformation characteristics such as a deformation modulus, a tangential and asecant elastic modulus, which are gained by uniaxial cyclic compression tests on mudstone and tuff, are compared with Ekb so as to corroborate the strain level of Ekb. Consequently, it is confirmed that the correlation coefficient 1.65 is proper for practical applications of the knocking ball test, and found that the secant elastic moduli of the mudstone and the tuff correspond roughly to Ekb at the axial strain of 0.35 % and 0.16 % for the mudstone and the tuff respectively.

Finally, the knocking ball tests are applied in two practices, one is to evaluate aggregate quality and the other is to determine the rock mass classification. The result of the first assignment shows that the knocking ball test is more useful and appropriate than the Schmidt impacthammer to obtain reliable elastic moduli of aggregates. And the result of the second assignment suggests that Ekb has a possibility to be a property which helps determine the rock mass classification promptly on site.

In conclusion, it is clear that the knocking ball test is the usable tool to evaluate the deformation characteristics.

Keywords:
Reservoir Characterization, classification, specimen, machine learning, Upstream Oil & Gas, quality evaluation, october 2014, Artificial Intelligence, mudstone, evaluation
Subjects:
Reservoir Characterization, Reservoir geomechanics
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2014. Japanese Committee for Rock Mechanics. Permission to distribute - International Society for Rock Mechanics
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