Image Recognition and Processing of Rock Cuttings Based on Computer Vision
In traditional oilfield logging work, the collected rock cuttings are affected by various factors such as the composition, structure, and microscopic characteristics of the formation due to various reasons. Moreover, the lithological characteristics of the strata are usually complex, and there are i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Well Logging Technology
2023-08-01
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| Series: | Cejing jishu |
| Subjects: | |
| Online Access: | https://www.cnpcwlt.com/#/digest?ArticleID=5515 |
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| Summary: | In traditional oilfield logging work, the collected rock cuttings are affected by various factors such as the composition, structure, and microscopic characteristics of the formation due to various reasons. Moreover, the lithological characteristics of the strata are usually complex, and there are issues with strong subjectivity, long time consumption, and large errors in manually distinguishing the types of rock cuttings. This will greatly reduce the accuracy of rock cuttings description. The OpenCV package in computer language is used to build computer vision, and the color tracker is built according to the HSV color model. By adjusting the color HSV value, different rock cuttings colors can be identified, and the area of rock cuttings with different colors can be calculated to achieve classification and fine recognition of rock cuttings images. Using the K-means clustering algorithm in data mining, rock cuttings data was classified and processed in batches to obtain HSV color ranges for different rock cuttings colors. Completed the feature information extraction of rock cuttings images and the classification and recognition of rock cuttings types. By analyzing and verifying the test set of rock cuttings sample data collected at a certain depth, comparing the results with the conclusions of logging data, it is determined that the accuracy rate of this method reaches 93.4%. The results indicate that the rock cuttings recognition and classification based on K-means optimization algorithm can accurately recognize and process rock cuttings images. Compared to manual methods, this method has higher accuracy in determining information such as formation lithology, oil reservoir location, and oil and gas bearing properties. It can provide more accurate stratigraphic information for oil exploration and development. |
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| ISSN: | 1004-1338 |