Methods, devices, electronic equipment and storage media for predicting the content of multi-component minerals

By employing a deep learning model that combines bidirectional multi-scale feature extraction with cross-scale attention fusion, the problem of global long-range dependence and local fine-grained features in multi-component mineral prediction in oil and gas reservoir exploration is solved. This model enables automatic learning of the mutual constraints between minerals, thereby improving prediction efficiency and accuracy.

CN121838918BActive Publication Date: 2026-05-26中国石油大学(北京)克拉玛依校区
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
中国石油大学(北京)克拉玛依校区
Filing Date
2026-03-13
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively handle the global long-range dependence and local fine-grained characteristics of multi-component minerals in oil and gas reservoir exploration, and they neglect the symbiotic relationships between minerals, resulting in a lack of consistency in the prediction results in terms of geological logic.

Method used

A deep learning model combining bidirectional multi-scale feature extraction and cross-scale attention fusion is employed, along with a 4Mamba-attention encoder and a mineral-guided Transformer decoder. The model is optimized through a joint loss function to achieve efficient prediction of the content of multi-component minerals.

Benefits of technology

It achieves accurate capture of global long-term trends and local abrupt fluctuations in well logging curves, automatically learns the mutual constraints between minerals, improves prediction efficiency and accuracy, and is applicable to oil and gas exploration and development and reservoir evaluation.

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Abstract

This invention relates to the field of oil and gas reservoir exploration technology, specifically a method, apparatus, electronic device, and storage medium for predicting the content of multiple minerals. The method includes acquiring input features of the well section to be predicted; inputting these features into a multi-component mineral content prediction model; and outputting the corresponding prediction results for the content of each mineral component. The multi-component mineral content prediction model is trained using multiple samples on a pre-set deep learning model, which employs a strategy of bidirectional multi-scale feature extraction and cross-scale attention fusion. The model constructed by this invention can not only efficiently capture the global long-term trend and local abrupt fluctuations of logging curves, but also automatically learn the mutual constraints between minerals, achieving accurate mapping between multi-scale logging features and specific mineral categories. Therefore, the multi-component mineral content prediction model enables efficient and collaborative prediction of multiple minerals, providing an effective data foundation for oil and gas exploration and development, reservoir evaluation, and production capacity prediction.
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