Fluorescence Methods for Downhole Fluid Analysis of Heavy Oil Emulsions

Andrews, A. Ballard; Schneider, Marc; Canas, Jesus Alberto; Freitas, Evie; Song, Yi‐Qiao; Mullins, Oliver C.

doi:10.1080/01932690701706839

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…Representative spectra of such emulsions are shown in Figure for the South American crude oil, where the average optical density of the emulsion can be seen to increase with water fraction. The optical absorption of the asphaltenes is exponential in this spectral range, but emulsions can give rise to both wavelength-dependent and wavelength-independent light scattering depending upon the droplet size . The predominant optical effect from low-water concentration emulsion formation here is strong wavelength-independent light scattering as the emulsified oil contains water droplets equal to or greater than the wavelength of light as verified by optical microscopy.…”

Section: Resultsmentioning

confidence: 66%

“…The concentration of asphaltenes is known to linearly impact the coloration of both crude oil and asphaltene/toluene solutions. , As such, VIS−NIR spectroscopy is an excellent method for quantifying the potential removal of asphaltenes by ultrafiltration. While some optical properties of crude oils such as fluorescence can be insensitive to emulsion formation, it is well-known that optical transmission measurements in the visible and near infrared are exquisitely sensitive to emulsion formation . Consequently, isolation of pure crude oil samples from emulsions can be monitored by VIS−NIR spectroscopy with great sensitivity.…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, isolation of pure crude oil samples from emulsions can be monitored by VIS−NIR spectroscopy with great sensitivity. The predominant effect of emulsions on VIS−NIR spectra is to cause light scattering that is largely wavelength-independent while the VIS−NIR spectra of asphaltenes are described by the Urbach tail formalism and depend exponentially on photon energy . Thus, the effect of emulsions and of asphaltenes can be separated out for almost all cases.…”

Section: Methodsmentioning

confidence: 99%

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On the Nanofiltration of Asphaltene Solutions, Crude Oils, and Emulsions

et al. 2010

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It has long been established that crude oils contain a colloidal suspension of asphaltenes where the mass fraction can range from 0 to 15%. The size of the colloidal particles is less well understood. Crude oil properties depend critically on the asphaltene content, yet measurement of these properties can be hindered by emulsified water and inorganic particulates in heavier crude oils. Here we explore the ability to isolate pure and unaltered crude oil samples using Gore-Tex membranes with nominal pore sizes as small as 30 nm where a transmembrane differential pressure no greater than 60 psi is employed. We first demonstrate that conventional crude oils can pass through a Gore-Tex membrane without alteration of their asphaltene content or their transport properties. Second, we demonstrate that the same filtration process can be used to extract crude oil from a water-in-oil emulsion where the extracted crude's properties are not distinguishable from those of the original crude. Third, we show that these experiments put an upper bound on the asphaltene nanoaggregate size in crude oils. For all experiments, visible-near infrared (VIS-NIR) spectroscopy and Karl Fischer titration analysis are used to determine the potential loss of asphaltenes upon filtration and the effectiveness of the removal of water from emulsions. In all cases, negligible asphaltene loss was detected, indicating that asphaltene nanoaggregates and nanoaggregate clusters are smaller than the 30 nm nominal pore size of the membranes used here. While only small sample volumes were used, membrane separation of pure crude oil is an excellent method for isolating pure, representative crude oil samples.

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Section: Resultsmentioning

confidence: 66%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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On the Nanofiltration of Asphaltene Solutions, Crude Oils, and Emulsions

et al. 2010

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“…For example, Fourier transform infrared (FTIR) spectrometers enable selective measurements under laboratory or mild environmental conditions, but are not typically suited for down hole measurements. Alternate spectroscopic tools based on narrow optical bandpass filters or a combination of bandpass filters with gratings have had some success under down hole conditions. ,,,, …”

mentioning

confidence: 99%

“…Alternate spectroscopic tools based on narrow optical bandpass filters or a combination of bandpass filters with gratings have had some success under down hole conditions. 2,8,16,36,37 The ability of an instrument to operate under harsh conditions has previously come at the cost of degraded performance relative to a laboratory FTIR. In this manuscript we characterize performance by the RMS prediction error (RMSE), which we define as the root-mean-square deviation between the concentration predicted by either a PLS or a MOC model and the actual sample concentration generated by the sample makeup conditions or a reference laboratory result using gas chromatography.…”

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confidence: 99%

In Situ Methane Determination in Petroleum at High Temperatures and High Pressures with Multivariate Optical Computing

et al. 2019

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Multivariate optical computing (MOC) is a compressed sensing technique enabling the measurement of analytes in a complex interfering mixture under harsh conditions. In this work, we describe the design, modeling, fabrication, and validation of a sensor for the measurement of dissolved methane in petroleum crude oil at high and variable combinations of pressure (up to 82.727 MPa) and temperature (up to 121.1 °C). Both laboratory and field validation results are presented, with five separate MOC sensors yielding a RMS error of 0.0089 g/cm3 methane in high pressure/high temperature laboratory and field samples compared to 0.0086 g/cm3 methane for a room temperature laboratory Fourier transform infrared (FTIR) spectrometer using partial least-squares (PLS) regression models.

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