Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares

Zhongbing Li, Wei Pang, Haibo Liang, Guihui Chen, Hongming Duan and Chuandong Jiang
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Zhongbing Li: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China
Wei Pang: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China
Haibo Liang: School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
Guihui Chen: School of Engineering, Southwest Petroleum University, Nanchong 637000, China
Hongming Duan: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China
Chuandong Jiang: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China

Energies, 2022, vol. 15, issue 4, 1-19

Abstract: Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology.

Keywords: gas logging; infrared spectroscopy; fast quantitative modelling; adaptive step sliding; partial least squares (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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