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Children at play often make suspensions such as mixtures of mud and water, flour and water, or a suspension of solid pigments in water known as tempera paint. These suspensions are heterogeneous mixtures composed of relatively large particles that are visible to the naked eye or can be seen with a magnifying glass. They are cloudy, and the suspended particles settle out after mixing. On the other hand, a solution is a homogeneous mixture in which no settling occurs and in which the dissolved...

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Updated: Jul 21, 2025

Chemical Analysis of Water-accommodated Fractions of Crude Oil Spills Using TIMS-FT-ICR MS

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Published on: March 3, 2017

Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms.

Alexey N Beskopylny¹, Anton Chepurnenko², Besarion Meskhi³

¹Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, 344003 Rostov-on-Don, Russia.

Biomimetics (Basel, Switzerland)

|July 28, 2023

Summary

This study introduces a computer vision algorithm using YOLOv4 to detect water globules in oil samples. The developed model accurately analyzes particle size and distribution, aiding oil refining quality control.

Keywords:

artificial intelligence computer vision convolutional neural network detection oil oil dehydration water globules

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Area of Science:

Petroleum Engineering
Computer Vision
Materials Science

Background:

Accurate fluid particle detection is crucial for oil and gas industry operations, impacting refining techniques and equipment quality assessment.
Existing methods for analyzing water globules in oil can be labor-intensive and may lack precision.

Purpose of the Study:

To develop and validate a computer vision algorithm for detecting and analyzing water globules in oil samples.
To assess the performance of a Convolutional Neural Network (CNN) based model for fluid particle analysis.

Main Methods:

Development of a custom dataset of microphotographs from oil refinery samples.
Implementation of the YOLOv4 Convolutional Neural Network (CNN) algorithm for object detection.
Application of an authors' augmentation algorithm to increase the training dataset size.
Dispersion analysis and frequency diagram generation for particle size distribution.

Main Results:

The developed YOLOv4-based algorithm accurately detects water globules in oil samples.
The model achieved high accuracy metrics: AP@50 = 89% and AP@75 = 78%.
Dispersion analysis provided insights into the size and number distribution of detected water particles.

Conclusions:

The CNN-based model is verified and suitable for detecting particles in fluid media.
The algorithm offers a reliable and accurate method for evaluating fluid quality and refining processes.
The developed tool provides researchers with controllable accuracy for particle detection.