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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. [ABSTRACT FROM AUTHOR]

• Quantitative analysis of spectra with a large-span. • Quantitative analysis method of mixed component spectroscopy. • Selection of calibration subset for quantitative analysis of large-span Spectroscopy. • A new measurement method for gas logging. As an important part of integrated logging, gas logging can directly measure the content and composition of hydrocarbon gas in the formation, and plays an irreplaceable role in the discovery and evaluation of oil and gas while drilling and real-time drilling parameter monitoring. Traditional gas logging uses chromatographs to analyze downhole gas. However, due to its long measurement period, many additional instruments, long lines, strict environmental requirements, complex operations, and easy distortion, it has been difficult to meet the requirements of current exploration work in complex oil and gas reservoirs. With the advantages of fast analysis speed, rich detection parameters, short delay time, good stability, and strong anti-pollution ability, the quantitative infrared spectroscopy technique can replace chromatography for hydrocarbon gas measurement. However, when it is used for the quantitative analysis of downhole hydrocarbon gases, a large span of absorption spectra and overlapping characteristic peaks can occur due to the complexity of gas components, making quantitative identification more difficult. To address the above problems, this paper proposes a method of net signal analysis (NAS) combined with density peak clustering (DPCA) for local PLS modeling. The method first obtains the net signals of the unknown and correction samples by the net signal analysis and then clusters the net signals by DPCA to select the corresponding local correction for PLS modeling prediction. The experimental results show that the NAS-DPCA-PLS method proposed in this paper is significantly better than the traditional method in the quantitative analysis of hydrocarbon mixed gas components, and the average accuracy of mixed component prediction reaches more than 98%, which effectively improves the detection accuracy of quantitative analysis of mixed component infrared spectroscopy and provides important theoretical support for the use of infrared spectroscopy in gas logging. [ABSTRACT FROM AUTHOR]