Lithology identification algorithm of cuttings based on one-dimensional convolutional neural network for mineral element content data

Ronghui Yan; Zijian Huang; Tieyuan Fang; Haitao Wang; Chen Li

doi:10.1117/12.3009451

16 October 2023 Lithology identification algorithm of cuttings based on one-dimensional convolutional neural network for mineral element content data

Ronghui Yan, Zijian Huang, Tieyuan Fang, Haitao Wang, Chen Li

Author Affiliations +

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030A (2023) https://doi.org/10.1117/12.3009451
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

The lithology identification of cuttings based on mineral element content data plays an important role in oil and gas exploration. Currently, the method for acquiring the element content of cuttings is to air-dry cuttings obtained in the mud logging process, and then use x-ray fluorescence (XRF) technology to obtain the types and contents of the main elements in the cuttings. In this paper, a method for identifying the lithology of cuttings based on channel attention mechanism is proposed for the mineral element content data of cuttings obtained by XRF technology. Specifically, the existing one-dimensional data composed of mineral elements are input into the network model. The channels are first expanded to introduce more features. Then, the features obtained by the multi-channels are fused to obtain features that are more conducive to the identification of cuttings lithology. To avoid introducing too much noise during channel change, the SE module is improved and applied to the one-dimensional convolutional neural network in this paper. Additionally, the features of different channels are weighted by autonomous learning, ensuring that the features related to the current task have a higher contribution to the network. By reducing the influence of invalid features caused by changing channels, this method safeguards the reliability of the features used for debris classification. The experiment results show that the cuttings recognition algorithm proposed in this paper has higher accuracy than the comparison algorithm.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ronghui Yan, Zijian Huang, Tieyuan Fang, Haitao Wang, and Chen Li "Lithology identification algorithm of cuttings based on one-dimensional convolutional neural network for mineral element content data", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030A (16 October 2023); https://doi.org/10.1117/12.3009451

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KEYWORDS

Convolution

Machine learning

Education and training

Data modeling

Neural networks

Convolutional neural networks

Detection and tracking algorithms

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