多层非线性局部感受野极限学习机方法用于录井气体分析  

Multi-layer nonlinear local receptive field extreme learning machine method for logging gas analysis

作　　者：李忠兵 袁章雨 梁海波[2] 谌贵辉[1] 蒋川东 Li Zhongbing;Yuan Zhangyu;Liang Haibo;Chen Guihui;Jiang Chuandong(School of Electrical Engineering and Information,Southwest Petroleum University,Chengdu 610500,China;School of Mechatronic Engineering,Southwest Petroleum University,Chengdu 610500,China)

机构地区：[1]西南石油大学电气信息学院,成都610500 [2]西南石油大学机电工程学院,成都610500

出　　处：《仪器仪表学报》2024年第3期157-169,共13页Chinese Journal of Scientific Instrument

基　　金：油气藏地质及开发工程国家重点实验室开放基金(PLN2022-42);国家自然科学基金(52074233);油气生产安全与风险控制重庆市重点实验室开放基金(cqsrc202101)项目资助。

摘　　要：随着我国能源需求的不断提升以及钻探环境的日益复杂化,开展高精度的烷烃类气体浓度检测对于提高油气勘探效率具有重要意义。光谱录井技术具有烃类气体检测快速、准确等优势,已成为石油勘探过程中备受关注的研究热点。针对录井气体红外光谱由于饱和吸收、噪声干扰、基线漂移等方面引起的非线性问题,提出了多层非线性局部感受野极限学习机(NM-LRF-ELM)模型。该模型将一维光谱数据转换为二维矩阵格式,利用局部感受野的数据处理方式在输入与隐藏层之间实现非线性特征提取。同时,引入改进的T-sigmoid激活函数,并在全连接层后加入dropout层来降低模型的过拟合风险。模型的特征提取与定量分析呈一体化结构,直接输出定量分析预测值。采集了两组共407个混合烷烃气体样本的红外光谱作为实验数据集,进行定量分析实验。实验结果表明,相较于滑动窗口类与灰狼优化定量分析模型,该模型的训练时间显著减少了90%以上。即使在同系物的非线性干扰下,模型的预测精度仍低于系统误差。因此,提出的方法有助于在现场环境变化复杂的情况下,降低未知气体的非线性干扰,提高对目标气体的红外光谱检测精度。With China′s increasing energy demand and the complex drilling environment,it is of great significance to carry out high-precision detection of alkane gas concentration to improve oil and gas exploration efficiency.Spectral logging technology has become a research hotspot in the process of oil exploration with the advantages of quick and accurate recording results.In this article,a multi-layer nonlinear local receptive field extreme learning machine(NM-LRF-ELM)model is proposed for resolving nonlinear problems caused by saturation absorption,noise interference,and baseline drift.The model converts one-dimensional spectral data into two-dimensional matrix format and realizes nonlinear feature extraction between input and hidden layer by using local receptive field data processing.Meanwhile,an improved T-sigmoid activation function is introduced and the dropout layer is added after the fully connected layer to reduce the overfitting risk of the model.The feature extraction and quantitative analysis of the model show an integrated structure and directly outputs the predicted value of quantitative analysis.In this article,the infrared spectra of 407 mixed alkane gas samples from two groups are collected as an experimental data set for quantitative analysis.The experimental results show that the training time of this model is reduced by more than 90%compared with the sliding window model and the gray Wolf model,and the prediction accuracy of the model is still lower than the system error under the nonlinear interference of the homolog.Therefore,the proposed method is helpful in reducing the nonlinear interference of unknown gas and improve the infrared spectrum detection accuracy of target gas under the condition of complex field environment changes.

关 键 词：气测录井 红外光谱 定量分析 局部感受野极限学习机 

分 类 号：TH741[机械工程—光学工程]