Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-40309 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 21 article 1072 pages: 285-299

July Andrea Gomez Camperos*
Mechanical Engineering Department, Universidad Francisco de Paula Santander, Seccional Ocaña, Vía Acol-sure, Sede el Algodonal Ocaña, Ocaña 546552, Colombia

Carlos Mauricio Ruiz Diaz
Mechanical Engineering Department, Industrial Multiphase Flow Laboratory (LEMI), São Carlos School of Engineering (ESSC), University of São Paulo (USP), Av. Trab. São Carlense, 400 - Parque Arnold Schimidt, São Carlos - SP, 13566-590, Brazil

Marlon Mauricio Hernández Cely
Control and Automation Engineering, Engineering Center, Federal University of Pelotas, Rua Benjamin Con-stant, n° 989, Porto, Pelotas - RS, 96010-020, Brazil

In this work, an application of artificial intelligence in the oils & gas industry is developed to iden-tify flow patterns in horizontal and vertical pipes of two-phase flow of oil and water, normalizing the word information and converting it to numerical values through the development of an artifi-cial neural network, whose input layer is composed of the surface velocities of each fluid, the ve-locity of the mixture, the volumetric fraction of the substances, diameter and the inclination of pipelines and the oil viscosity. The Artificial Neural Networks (ANN) has two hidden layers composed of 45 neurons. The database with which the model was trained, validated, and tested has 6993 rows of information corresponding to the inputs of the intelligent system and particular-ized for annular flow in horizontal pipes and DO/W in vertical pipelines. Notice that the infor-mation was obtained after re-engineering the information presented by 12 and 18 authors for hor-izontal and vertical piping, respectively. Finally, the mean square error obtained by the model was around 1.38%, with a maximum coefficient of determination of 0.79.

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