Machine Learning Application to Predict the Efficiency of Water Coning Prevention Techniques Implementation

Veliyev, Elchin und Aliyev, Azizagha und Mammadbayli, Toghrul (2021) Machine Learning Application to Predict the Efficiency of Water Coning Prevention Techniques Implementation. SOCAR Proceedings, 2021 (1). Seiten 104-113. ISSN 2218-8622

PDF (Machine Learning Application to Predict the Efficiency of Water Coning Prevention Techniques Implementation) - Veröffentlichte Version Herunterladen (722kB)

Kurzfassung

The increase in number of the mature fields is accompanied by an increase in the water cut of the produced fluids. One of the most common causes of this phenomenon is the process of water coning, that is, the breakthrough of the bottom water to the wellbore, in which water flows form a figure similar to a cone. The paper proposes a ranking mechanism based on machine learning methods that allow to significantly reduce the resource intensity of existing prediction models. In order to preserve the simplicity of presentation, the proposed mechanism is considered on the example of one technology - DWL. Obtained results show about 10% smaller deviation values when using the least squares support vector machine in comparison with the ANN. Both developed models demonstrated acceptable results for practical application.

Eintragstyp:	Artikel
Stichwörter:	Water coning, Artificial neural network, Least square support vector machine, Particle swarm optimization method, Prediction
Benutzer:	Azizagha Aliyev
Hinterlegungsdatum:	13 Okt 2022 18:15
Letzte Änderung:	13 Okt 2022 18:15
URI:	https://eprints.dbges.de/id/eprint/1838

Aktionen (Anmeldung erforderlich)

Eintrag anzeigen