Diseño de un modelo machine learning para la selección del mejor mecanismo de control de arena entre los mecanismos zeta flow y high rate water pack en el campo X de los Llanos Orientales de Colombia
A formation with a high presence of fines represents a problem in the production plan of a field, leading to cost overruns due to damage to production lines and equipment. The sand control method selected depends on the specific conditions of the work area, operating practices, and economic consider...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad de América
- Repositorio:
- Lumieres
- Idioma:
- spa
- OAI Identifier:
- oai:repository.uamerica.edu.co:20.500.11839/8264
- Acceso en línea:
- https://hdl.handle.net/20.500.11839/8264
- Palabra clave:
- Control de arena
Machine Learning
Mecanismos zeta
Sand control
Machine learning
Zeta mechanisms
Tesis y disertaciones académicas
- Rights
- License
- Atribución – No comercial
| Summary: | A formation with a high presence of fines represents a problem in the production plan of a field, leading to cost overruns due to damage to production lines and equipment. The sand control method selected depends on the specific conditions of the work area, operating practices, and economic considerations. The correct selection of the sand control method allows to moderate or mitigate the negative effects of a high BSW. In this sense, the objective of this research is to design a predictive model using Machine Learning for the selection of the best sand control mechanism between the Zeta Flow and High Rate Water Pack mechanisms in field X, achieving with this model the production reduction of sand. To carry out this purpose, a predictive model based on the Python programming language was developed, which interprets the operational parameters and work history and assimilates them as training to arrive at the most accurate prediction. |
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