Multivariate Analysis of Crude Oil Composition and Fluid Properties Used in Multiphase Flow Metering (MFM)

Abstract

Crude oil characterization by infrared (IR) spectroscopy and whole oil gas chromatography (GC) has been used to provide data for establishing multivariate prediction models for physical properties of crude oils. The parameters of interest are used in multiphase flowmeters (MFMs) for monitoring production and transport of petroleum fluids, and permittivity parameters are of special interest. Data for 20 crude oils and condensates has been acquired and modeled using partial least squares (PLS) modeling. Good quality predictions were obtained for density, velocity of sound, and static and high frequency permittivity. Biodegradation of crude oil is the main cause of variation in the modeled variables. The data required are obtained in standard analytical procedures, and thus the approach has a considerable potential for use in on-site quality assurance.

Predicting fuel research octane number using Fourier-transform infrared absorption spectra of neat hydrocarbons

“…The approaches of Kelly et al [4] and other work in this area [5][6][7][8][9][10][11][12][13][14][15][16][17] used real-world fuel samples to inform the optics-based statistical models. This work instead used neat hydrocarbons-six of them are primarily utilized as constituents in gasoline surrogates, and 28 being the primary constituents in the FACE gasolines-as well as mixtures that contain the primary six components.…”

“…The more recent work of Kardamakis and Pasadakis [8] presents an efficient multivariate analysis technique that predicts RON within ±0.26 using a limited data set in comparison to previous studies; this work also provides a succinct history of efforts in this field. There are many additional studies to the short list previously mentioned that consider various optical and multivariate analysis techniques to predict performance parameters of fuels [9][10][11][12][13][14][15][16][17].…”

“…All the previous approaches using multivariate analysis to predict fuel attributes [4][5][6][7][8][9][10][11][12][13][14][15][16][17] used existing, real-world fuel samples (i.e., existing gasoline, diesel, jet fuels) as the training data set to predict performance attributes of those specific fuels. This work used hydrocarbons-neat or combined as mixtures for gasoline surrogate fuels including up to five neat components-to provide model input for predicting RON of the Fuels for Advanced Combustion Engines (FACE) gasolines designed by the Coordinating Research Council (CRC) and manufactured by ChevronPhillips Chemical Co [20].…”

Classification of vinegar with different marked ages using olfactory sensors and gustatory sensors

“…Among them, PCA and LDA, as two well-known pattern recognition methods, were used for the data fusion, which consists of 60 data samples with 20 variables from the gustatory sensor system and 45 variables from the olfactory sensors. 22 Since the current Eigenvalues may not at the same order of magnitude, normalization was required to transform these data into a common level prior to multivariate analysis. 23 The most commonly used score normalization technique is the z-score that is calculated using the arithmetic mean and standard deviation of the given data.…”