Research on quantitative analysis method of PLS hydrocarbon gas infrared spectroscopy based on net signal analysis and density peak clustering. (January 2022)

• Record Type: Journal Article
• Authors: Liang, Haibo; Liu, Gang
• Is Part Of: Measurement. Volume 188(2022)
• Publication Date: 2022-01
• Subjects: Gas logging -- Infrared absorption spectroscopy -- NAS -- DPCA -- PLS -- Hydrocarbon component detection; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110392
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: 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. Abstract: 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.