Vol. 36 No. 3 (2023): Revista ION
Articles

Technical-economic feasibility of implementation of a virtual sensor for production in wells of a field in Valle Medio del Magdalena in Colombia

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  • Giovanni Vizcaya-Cedeño ,
  • Fernando Enrique Calvete González,
  • Giovanni Morales Medina
Giovanni Vizcaya-Cedeño
ECOPETROL S.A.
Fernando Enrique Calvete González
Universidad Industrial de Santander
Giovanni Morales Medina
Universidad Industrial de Santander
DOI: https://doi.org/10.18273/revion.v36n3-2023006

Published 2023-12-12

Keywords

  • Virtual sensor,
  • Artificial neural network,
  • Production oil well,
  • Valle Medio del Magdalena,
  • Neuralnet package,
  • CAPEX
    • ...More
    Less

How to Cite

Vizcaya-Cedeño , G. ., Calvete González, F. E. ., & Morales Medina, G. (2023). Technical-economic feasibility of implementation of a virtual sensor for production in wells of a field in Valle Medio del Magdalena in Colombia. Revista ION, 36(3), 63–74. https://doi.org/10.18273/revion.v36n3-2023006

Copyright (c) 2023 Giovanni Vizcaya-Cedeño , Fernando Enrique Calvete González, Giovanni Morales Medina

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Production professionals utilize well performance data from physical sensors to identify wells that require corrective maintenance to normalize production flows. The foregoing corresponds to support for the completion of corporate goals. However, the costs of physical sensors may restrict access to the required information in real-time. For this, virtual sensors (VS) arise as a low-cost alternative, which can predict production flows, based on data available from physical sensors and on procedures embedded in artificial neural networks (ANN). Herein, we expose a technical-economic feasibility analysis of the implementation of a production VS in wells with a Progressive Cavity (PCP) artificial lift system in a field in the Valle Medio del Magdalena of Colombia (VMM). For this, production data with 15 variables were used in the training and validation of different ANN architectures, according to the codes of the Neuralnet library of the free software environment R. Likewise, an evaluation of the economic impact derived from the implementation of the VS in wells of the VMM field is disclosed. According to the results, the ANN with an inner layer of 23 neurons and logistic activation functions reported the best prediction performance, with errors of ± 9,6 at 95 % confidence. On the other hand, the application of a VS based on the RNA for the wells with PCP of the VMM field would lead to a favorable economic benefit, with a net present value of $25,5 MMusd, considering a 10-year cash flow.

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