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.
In the oil and gas industry, it is crucial to employ appropriate drilling fluids to maintain equilibrium of formation pressure throughout the various stages of drilling operations. During the recycling process, the drilling fluid may precipitate gas and as a result, exhibit non-full pipe flow upon return to the surface. Accurate measurement of the volume flow rate of it is imperative in obtaining valuable information from the bottom of the well. Commonly, on-site drilling operations use a multiphase target flowmeter in conjunction with an empirical model to rectify calculation results. Therefore, the theoretical potential of utilizing non-contact ultrasonic sensors for measuring multiphase volume flow rate of non-full pipe flow is significant. In this research, an apparent flow velocity calculation model was established by integrating the ultrasonic Doppler shift model and pipeline fluid mechanics utilizing a four-channel ultrasonic array. Subsequently, the invariant scattering convolution -long short-term memory network was trained on the data-fused ultrasonic signal to identify the liquid level. The velocity-area method was also employed to establish a new multiphase volume flow calculation model. To evaluate the validity of the proposed model, comparison experiments of liquid single-phase flow and liquid-solid two-phase flow were conducted. The experimental results show that, compared with the comparative flow measurement system (CFMS), the accuracy of the ultrasonic flow measurement system (UFMS) is reduced by 0.965%, the nonlinear error (NLE) by 2.293 %, the average relative error (ARE) by 2.570 %, the standard deviation (SD) by 1.395, and the root mean square error (RMSE) by 14.394.
When traditional infrared spectroscopy technology measures and analyzes the quantitative research of fluid, due to the complex band composition of infrared spectrum, weak absorption intensity and spectral signal, it is easy to be disturbed during measurement and increase the error, making the quantitative analysis of fluid inaccurate and difficult to achieve requirements for accurate analysis of fluids. In order to solve this problem, this paper uses the self-developed experimental apparatus, and takes water-based drilling fluid and diesel oil as the research objects, and proposes a new method for quantitative research of two-component proportional fluids based on HDC-PSAM. A large number of experiments are carried out on water-based drilling fluids, diesel and water mixtures, and a large number of spectral experimental data are preprocessed using moving window smoothing and normalization methods. Extracting effective features with Hybrid dilated convolution (HDC) and Pyramid self-attention modules (PSAM) , combined with Partial Least Squares Algorithm (PLS) to model fluid mixtures to achieve quantification of fluid mixtures calculate. The experimental results show that this method can reduce the influence of disturbance during measurement, has higher accuracy and stability, and effectively improves the measurement accuracy of infrared spectroscopy for quantitative analysis of fluids. At the same time, this method provides a strong technical support for the analysis of other fluids and the measurement of fluid mixture by using infrared spectroscopy technology.
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