数据采集与处理2025，Vol.40Issue(3)：793-806,14.DOI:10.16337/j.1004-9037.2025.03.018
页岩气核心参数预测的异构异质数据多模态融合算法
Multi-modal Fusion Algorithm for Heterogeneous Data of Shale Gas Core Parame-ters Prediction
摘要
Abstract
Unlike traditional multi-modal fusion methods that are predominantly image-based,production data in industrial manufacturing are primarily structured data,with a small amount of image features.However,both types of heterogeneous data reflect the core parameters of shale gas.Due to the significant difference in data dimensions,it is challenging to achieve feature fusion of heterogeneous data.Additionally,there is heterogeneity among the stratified structured data,leading to substantial errors in predicting core parameters using conventional deep learning methods.To address these issues,this paper proposes a multi-modal fusion algorithm for heterogeneous data(MFH).Firstly,a multi-modal fusion strategy for heterogeneous data is designed to align,extract,and merge features of scanning electron microscopy and logging parameters under the same depth labels.Secondly,a mechanism for drawing heterogeneous data features closer is constructed to create positive sample pairs,enabling the model to learn about the strong heterogeneity between stratums in the same work area and the lateral nonlinear relationships.Finally,a method for exchanging features of heterogeneous data is introduced to solve the matching problem between abundant logging data and scarce electron microscope images,achieving accurate and continuous prediction of core parameters.Experimental results,compared with predictions from mainstream deep models,prove the practicality,effectiveness,and extensibility of the proposed scheme.关键词
多模态融合/特征拉近机制/异构数据/异质性/注意力机制Key words
multi-modal fusion/feature alignment mechanism/heterogeneous data/heterogeneity/attention mechanism分类
信息技术与安全科学引用本文复制引用
罗浚七,汪敏,乔豁通,邱毅,张浩洋,孙活,谢浩宇..页岩气核心参数预测的异构异质数据多模态融合算法[J].数据采集与处理,2025,40(3):793-806,14.基金项目
国家自然科学基金(62006200) （62006200）
中国石油-西南石油大学创新联合体科技合作项目(2020CX020000) （2020CX020000）
四川省科技计划支持项目(2022YFG0179). （2022YFG0179）
