DBGPrints Repository
Publications of the German Soil Science Society

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

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

[img] PDF (Machine Learning Application to Predict the Efficiency of Water Coning Prevention Techniques Implementation) - Published Version
Download (722kB)

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Water coning, Artificial neural network, Least square support vector machine, Particle swarm optimization method, Prediction
Depositing User: Azizagha Aliyev
Date Deposited: 13 Oct 2022 18:15
Last Modified: 13 Oct 2022 18:15
URI: https://eprints.dbges.de/id/eprint/1838

Actions (login required)

View Item View Item