Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs
dc.contributor.advisor | Ochoa Gutiérrez, Luis Hernán | |
dc.contributor.author | Leal Freitez, Jorge Alberto | |
dc.date.accessioned | 2021-11-24T20:07:06Z | |
dc.date.available | 2021-11-24T20:07:06Z | |
dc.date.issued | 2021-11 | |
dc.description | ilustraciones, fotografías color, fotografías blanco y negro, tablas | spa |
dc.description.abstract | The purpose of this research is to develop a methodology for automatic detection and classification of geologic planes in wireline acquired resistivity imaging. The methodology involves the application of bioinspired image filters, machine learning, and elements of fractal theory. In upstream activities of hydrocarbon reservoirs, the analysis and interpretation of resistivity imaging is a time-consuming and repetitive task, usually manually performed by specialized geologists. Delay in this task may result in operational problems, causing economic losses during field operations. In order to solve this issue, the present research proposes automatic extraction of geologic features in borehole resistivity imaging shortly after the image log is acquired. Features are initially extracted from pixels analysis of the dynamic-normalized images, employing a set of computer vision techniques to detect edges and sinusoids. Afterward, these sinusoids are classified using statistical measures and machine learning. During training and classification stages, machines employ fractal dimension, lacunarity, and their derived statistic parameters (fractal attributes); the fractal information is complemented with resistivity, gamma rays, and photoelectric factor logs to improve the accuracy during classification. In both clastic and carbonate environments, the outcome consists of a set of classified geologic planes, precise enough to recognize patterns in the structural and stratigraphic dips of the drilled sequence. Academically, the main novelty of this research is the integration of fractal theory and machine learning, aiming at automatizing interpretation of resistivity imaging in hydrocarbon producer wells. (Text taken from source) | eng |
dc.description.abstract | El propósito de esta investigación es desarrollar una metodología para la detección y clasificación automática de planos geológicos en imágenes resistivas adquirida con cable. La metodología involucra aplicación de filtros bioinspirados, aprendizaje automático y elementos de la teoría fractal. En las actividades aguas arriba de yacimientos de hidrocarburos, el análisis e interpretación de imágenes resistivas es una tarea repetitiva, que requiere mucho tiempo y generalmente realizada manualmente por geólogos especializados. La demora en esta tarea puede resultar en problemas operativos, causando pérdidas económicas durante las operaciones de campo. Para resolver este problema, la presente investigación propone la extracción automática de características geológicas en las imágenes de resistividad del pozo poco después de que se adquiere el registro de imágenes. Las características se extraen inicialmente del análisis de píxeles de las imágenes dinámicas normalizadas, empleando un conjunto de técnicas de visión por computadora para detectar bordes y sinusoides. Posteriormente, estas sinusoides se clasifican utilizando medidas estadísticas y aprendizaje automático. Durante las etapas de entrenamiento y clasificación, las máquinas emplean la dimensión fractal, lagunaridad y sus parámetros estadísticos derivados (atributos fractales); la información fractal se complementa con registros de resistividad, rayos gamma y factor fotoeléctrico para mejorar la precisión durante la clasificación. Tanto en ambientes clásticos como en carbonatos, el resultado consiste en un conjunto de planos geológicos clasificados, lo suficientemente precisos como para reconocer patrones en los buzamientos estructurales y estratigráficos de la secuencia perforada. Académicamente, la principal novedad de esta investigación es la integración de la teoría fractal y aprendizaje automático, con el objetivo de automatizar la interpretación de imágenes resistivas en pozos productores de hidrocarburos | spa |
dc.description.degreelevel | Doctorado | spa |
dc.description.degreename | Doctor en Geociencias | spa |
dc.description.researcharea | Sistemas Inteligentes Aplicados a las Geociencias | spa |
dc.format.extent | 136 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.identifier.instname | Universidad Nacional de Colombia | spa |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/80725 | |
dc.language.iso | eng | spa |
dc.publisher | Universidad Nacional de Colombia | spa |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
dc.publisher.department | Departamento de Geociencias | spa |
dc.publisher.faculty | Facultad de Ciencias | spa |
dc.publisher.program | Bogotá - Ciencias - Doctorado en Geociencias | spa |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.license | Reconocimiento 4.0 Internacional | spa |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | spa |
dc.subject.ddc | 550 - Ciencias de la tierra | spa |
dc.subject.ddc | 500 - Ciencias naturales y matemáticas | spa |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales | spa |
dc.subject.proposal | Oil and gas wells | eng |
dc.subject.proposal | Machine learning | eng |
dc.subject.proposal | Fractal dimension | eng |
dc.subject.proposal | Lacunarity | eng |
dc.subject.proposal | Borehole resistivity imaging | eng |
dc.subject.proposal | Geologic planes | eng |
dc.subject.proposal | automatic picking | eng |
dc.subject.proposal | Automatic dip classification | eng |
dc.subject.proposal | Hydrocarbon reservoirs | eng |
dc.subject.proposal | Aprendizaje automático | spa |
dc.subject.proposal | Dimensión fractal | spa |
dc.subject.proposal | Lagunaridad | spa |
dc.subject.proposal | Imágenes resistivas de pozo | spa |
dc.subject.proposal | Planos geológicos | spa |
dc.subject.proposal | Clasificación automática de buzamientos | spa |
dc.subject.proposal | Yacimientos de hidrocarburos | spa |
dc.title | Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs | eng |
dc.title.translated | Aprendizaje automático y atributos fractales aplicados a la detección y clasificación automática de planos geológicos en registros de imágenes resistivas | spa |
dc.type | Trabajo de grado - Doctorado | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_db06 | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/doctoralThesis | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/TD | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
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