Intelligent lithology identification method for water-bearing rock combining image recognition and spectral analysis
By combining image recognition and spectral analysis, and using double transfer features to fuse convolutional neural networks, the problem of difficult identification of fine rock fragments has been solved, achieving more efficient lithology identification and oil and gas resource exploration.
CN115908890BActive Publication Date: 2026-07-03CHINA FRANCE BOHAI GEOSERVICES
2 Cites 0 Cited by
Patent Information
- Application Number
- CN202211192092.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-09-28
- Publication Date
- 2026-07-03
- Estimated Expiration
- 2042-09-28
AI Technical Summary
Technical Problem
Because the fine rock cuttings broken by PDC drill bits are difficult to identify with the naked eye, traditional manual description methods are ineffective in identifying lithology, resulting in low efficiency in oil and gas resource exploration.
Method used
By combining image recognition and spectral analysis, a lithological feature determination model is constructed using a double transfer feature fusion convolutional neural network. A lithological identification method is then developed by combining image feature recognition and spectral analysis.
Benefits of technology
It improves the accuracy of lithology identification and the efficiency of oil and gas resource exploration and development, especially with higher identification accuracy on small-scale datasets.
✦ Generated by Eureka AI based on patent content.
Abstract
The application provides a water-bearing rock intelligent lithology identification method combining image recognition and spectrum analysis, comprising the following steps: 1, obtaining original drilling while cutting image and rock infrared spectrum data; 2, performing image pretreatment on the original cutting image; 3, performing cutting sample image feature extraction and normalization processing; 4, performing feature selection on the water-bearing rock spectrum data by using the Filter method and performing normalization processing; 5, based on a double migration feature fusion convolutional neural network, combining the cutting sample image feature and the rock spectrum data to construct a cutting feature analysis model; 6, combining the cutting feature and the rock spectrum data to establish a lithology template library; and 7, performing lithology identification. The application can more accurately identify the lithology in the process of mud logging, and improve the exploration and development efficiency of oil and gas resources.
Need to check novelty before this filing date? Find Prior Art