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Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11839/8274Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Fernández Barrero, Nelson | - |
| dc.contributor.author | Martínez Alonso, Juan David | - |
| dc.contributor.author | Poveda Cruz, Johan Steven | - |
| dc.date.accessioned | 2021-04-03T23:26:15Z | - |
| dc.date.available | 2021-04-03T23:26:15Z | - |
| dc.date.issued | 2021-02-15 | - |
| dc.identifier.citation | APA 7th - Martínez Alonso, J. D. y Poveda Cruz, J. S. (2021) Optimización en la planeación de pozos por medio de la predicción de tiempos, costos y NPT´S, aplicando un modelo de machine learning para la campaña de perforación de Castilla y Castilla norte 2020. [Trabajo de grado, Fundación Universidad de América] Repositorio Institucional Lumieres. https://hdl.handle.net/20.500.11839/8274 | - |
| dc.identifier.uri | https://hdl.handle.net/20.500.11839/8274 | - |
| dc.description | Currently the company Ecopetrol SA, takes into account the technical-historical data such as those stored in OpenWells and Power BI, to evaluate the performance during the drilling phase of the wells week by week, instead of taking advantage of this information together with the variables involved in the complexity matrix, to optimize well planning through the implementation of a predictive model, thus generating added value on the stored information. Considering the above, the present degree work was carried out in order to optimize well planning for the 2020 Castilla y Castilla Norte drilling campaign by applying the selected machine Learning models, which predict cost days and NPT's associated with problems in open hole. Therefore, a methodology aimed at the implementation of three supervised machine learning models was designed, based on the information from the 2019 Castilla y Castilla Norte drilling campaign. Subsequently, the prediction of the models was implemented and evaluated. for the same field in 2020. | spa |
| dc.description.abstract | Actualmente la compañía Ecopetrol S.A, tiene en cuenta los datos técnicos-históricos tales como los que se encuentran almacenados en OpenWells y Power BI, para evaluar el desempeño durante la fase de perforación de los pozos semana a semana, en vez de aprovechar dicha información junto con las variables implicadas en la matriz de complejidad, para optimizar la planeación de pozos mediante la implementación de un modelo predictivo generando así un valor agregado sobre la información almacenada. Considerando lo anterior, el presente trabajo de grado se realizó con el fin de optimizar la planeación de pozos para la campaña de perforación de Castilla y Castilla Norte 2020 al aplicar los modelos de machine Learning seleccionados, los cuales predicen días costos y NPT’s asociados a problemas en hueco abierto. | spa |
| dc.language.iso | es | spa |
| dc.publisher | Fundación Universidad de América | spa |
| dc.rights | Atribución – No comercial – Sin Derivar | spa |
| dc.subject | Matriz complejidad | spa |
| dc.subject | Método supervisado | spa |
| dc.subject | Predicción de tiempos | spa |
| dc.subject | Complexity matrix | spa |
| dc.subject | Supervised method | spa |
| dc.subject | Time prediction | spa |
| dc.subject | Tesis y disertaciones académicas | spa |
| dc.title | Optimización en la planeación de pozos por medio de la predicción de tiempos, costos y NPT´S, aplicando un modelo de machine learning para la campaña de perforación de Castilla y Castilla norte 2020 | spa |
| dc.title.alternative | Optimization in well planning through the prediction of times, costs and NPT'S, applying a machine learning model for the 2020 Castilla and Castilla norte drilling campaign | spa |
| dc.type | bachelorThesis | spa |
| Appears in Collections: | Trabajos de grado - Ingeniería de Petróleos | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5152276-2021-1-IP.pdf | 2.45 MB | Adobe PDF | View/Open | |
| CARTA DE CESIÓN DE DERECHOS Y AUTORIZACIÓN PARA PUBLICACIÓN.pdf Access Restricted | 183.68 kB | Adobe PDF | View/Open Request a copy |
