Estimation of mechanical properties of rock using artificial intelligence
This article presents the way two artificial intelligence techniques, neural networks and genetic algorithms were combined, for the development of a computational tool used to estimate mechanical properties such as tensile strength, uniaxial compression resistance and resistance to triaxial compress...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | Colombia |
| Institución: | Universidad EAFIT |
| Repositorio: | Repositorio EAFIT |
| Idioma: | español |
| OAI Identifier: | oai:repository.eafit.edu.co:10784/14465 |
| Acceso en línea: | http://hdl.handle.net/10784/14465 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial Intelligence Artificial Neural Network Genetic Algorithm Petrophysical Properties Mechanical Properties Inteligencia Artificial Red Neuronal Artificial Algoritmo Genético Propiedades Petrofísicas Propiedades Mecánicas |
| Sumario: | This article presents the way two artificial intelligence techniques, neural networks and genetic algorithms were combined, for the development of a computational tool used to estimate mechanical properties such as tensile strength, uniaxial compression resistance and resistance to triaxial compression in sandstones, from petrophysical properties using test data from the Rock Mechanics Laboratory of the Colombian Petroleum Institute - Ecopetrol SA as training data facilitating the design of non-destructive tests with a certain degree of confidence and leading to cost reduction. |
|---|