Abstract

The method of micro-macro decoupling analyses is applied to assess the macroscopically anisotropic strengths of alternate layers of inclined rock masses, which are characterized by the layered structure of sedimentary soft rocks, in consideration of yield and tensile/compressive strengths of rocks at micro-scale. To evaluate the macroscopic strength characteristics, a series of numerical material testing, which is based on the mathematical homogenization theory, is conducted on a unit cell composed of mudstone and sandstone, each of which exhibits consolidation-pressure dependent plastic deformations as well as tension-cut-off-type damage behavior. Numerical examples are presented to demonstrate the performance of the presented strength assessment scheme in view of design and analysis of important structures encountered in practical applications.

Keywords:
machine learning, structural geology, reservoir geomechanics, strength, Reservoir Characterization, Upstream Oil & Gas, Ishida, october 2014, material parameter, stress-strain curve
Subjects:
Reservoir Characterization, Information Management and Systems, Exploration, development, structural geology, Reservoir geomechanics, Artificial intelligence
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2014. Japanese Committee for Rock Mechanics. Permission to distribute - International Society for Rock Mechanics
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