Lithology Identification Method and Application Based on Generative Adversarial Neural Network
Lithology identification is the basis of reservoir evaluation and the key to reservoir parameter calculation and reservoir evaluation and development. Well logging data contains a lot of formation information, which is the basic data for lithology identification. However, the lithology interpretatio...
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| Main Author: | |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Well Logging Technology
2025-02-01
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| Series: | Cejing jishu |
| Subjects: | |
| Online Access: | https://www.cnpcwlt.com/en/#/digest?ArticleID=5706 |
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| Summary: | Lithology identification is the basis of reservoir evaluation and the key to reservoir parameter calculation and reservoir evaluation and development. Well logging data contains a lot of formation information, which is the basic data for lithology identification. However, the lithology interpretation of logging data is affected by many factors, which results in multiple solutions. The accuracy of lithology identification using traditional methods is often difficult to meet the requirements. Based on the advantages of machine learning in data analysis and modeling, two-step lithology identification strategy and generative adversarial neural network are used to identify the lithology of logging with unbalanced categories. Based on the logging data of Permian and Triassic sandstone reservoirs in Fukang depression, seven logging curves, including acoustic time difference, borehole diameter, neutron, density, natural gamma ray, formation resistivity and spontaneous potential, are selected as characteristic inputs through correlation of logging curves and petrophysical analysis. Six lithologies, namely mudstone, sandy mudstone, fine sandstone, medium sandstone, sand conglomerate and conglomerate, are identified. A good recognition effect has been obtained. According to the comparative test, the accuracy of two-step lithology identification is 4.21% higher than that of single step lithology identification. In addition, the accuracy of the two-step adjunct network model is 4.72%～7.19% higher than that of random forest, support vector machine, distributed gradient enhanced library and long short-term memory network model, and the overall recognition accuracy of the model reaches 83.44%. This method has a better development prospect in the field of lithology identification. |
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| ISSN: | 1004-1338 |