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.
Highlights
Gas logging refers to the process of measuring the type and content of hydrocarbon gas contained in the analysis of the drilling fluid generated during the oil and gas exploration process [1]
The stripe distributions of C1 ∼C5 are similar, but are obviously different from CO2. This indicates that the influence of the window position is much greater than the influence of the window width in the local partial least squares (PLS) modelling; the influence of the window position to C1 ∼C5 is alike, but is obviously different from CO2, which is determined by the same C-H and C-C chemical bond in C1 ∼C5 but the C=O chemical bond in CO2
This paper proposes a new adaptive step-sliding partial least squares (ASS-PLS) infrared spectroscopy gas-logging modelling method
Summary
Gas logging refers to the process of measuring the type and content of hydrocarbon gas contained in the analysis of the drilling fluid generated during the oil and gas exploration process [1]. At first, it was mainly used as a pure safety system designed to monitor the content of toxic or flammable substances. It has important guiding significance to accurately analyse and evaluate the oil and gas content and type of downhole drilled formations in real time, for the timely discovery and interpretation of the reservoir environment, improving the efficiency of oil and gas exploration and ensuring the safety of drilling operations [3]. Their research pointed out that it was the best way to increase the analysis speed by reducing the inner diameters of open-tube chromatographic columns and packed chromatographic columns, but the inlet pressure required would increase correspondingly, resulting in high flow resistance inside the chromatographic column, which would severely restrict the application of the chromatographic column in fast GC [9]
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