Técnicas de aprendizado de máquina para previsão de perdas severas em rochas carbonáticas de reservatórios do pré-sal

Translated title of the contribution: Machine learning techniques for predicting severe losses in carbonate rocks from pre-salt reservoirs

Giovani Ferreira Machado, Luciana Faletti Almeida, Juan Guillermo Lazo Lazo

Research output: Contribution to journalArticle in a journalpeer-review

Abstract

This work aims to present binary classification models to assist in determining the occurrence of circulation loss phenomenon in the construction of subsea wells in the Santos Basin’s pre-salt. Prior knowledge about the possibility of the phenomenon occurrence, makes it possible to allocate rigs with the appropriate technology for the well's construction. In this context, classification systems based on machine learning can support decision making. In this work, classifiers are proposed based on classic machine learning algorithms (Pedregosa, F. et al. 2011) and the results are presented using the Area Under the ROC Curve (AUC) as the metric.
Translated title of the contributionMachine learning techniques for predicting severe losses in carbonate rocks from pre-salt reservoirs
Original languagePortuguese
Pages (from-to)28061-28074
Number of pages14
JournalBrazilian Journal of Development
Volume7
Issue number3
DOIs
StatePublished - 1 Mar 2021
EventCongresso Brasileiro de Automática - Brasil
Duration: 11 Jan 202011 Jan 2020

Keywords

  • Circulation Loss
  • Classification
  • Machine learning
  • Pre-salt
  • Drilling

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