Differential pigmentation between phytoplankton allows use of fluorescence excitation spectroscopy for the discrimination and classification of different taxa. Here, we describe the design and performance of a fluorescence imaging photometer that exploits taxonomic differences for discrimination and classification. The fluorescence imaging photometer works by illuminating individual phytoplankton cells through an asynchronous spinning filter wheel, which produces bar code-like streaks in a fluorescence image. A filter position is covered with an opaque filter to create a reference dark position in the filter wheel rotation that is used to match each fluorescence streak with the corresponding filter. Fluorescence intensities of the imaged streaks are then analyzed for the purpose of spectral analysis, which allows taxonomic classification of the organism that produced the streaks. The theoretical performance and signal-to-noise ratio (SNR) specifications of these MOEs are described in Part I of this series. This report describes optical layout, flow cell design, magnification, depth of field, constraints on filter wheel and flow velocities, procedures for blank subtraction and flat-field correction, the measurement scheme of the instrument, and measurement of SNR as a measurement of filter wheel frequency. This is followed by an analysis of the sources of variance in measurements made by the photometer on the coccolithophore Emiliania huxleyi. We conclude that the SNR of E. huxleyi measurements is not limited by the sensitivity or noise attributes of the measurement system, but by dynamics in the fluorescence efficiency of the E. huxleyi cells. Even so, the minimum SNR requirements given in Part I for the instrument are met.

Phytoplankton are single-celled, photosynthetic algae and cyanobacteria found in all aquatic environments. Differential pigmentation between phytoplankton taxa allows use of fluorescence excitation spectroscopy for discrimination and classification. For this work, we applied multivariate optical computing (MOC) to emulate linear discriminant vectors of phytoplankton fluorescence excitation spectra by using a simple filter-fluorometer arrangement. We grew nutrient-replete cultures of three differently pigmented species: the coccolithophore Emiliania huxleyi, the diatom Thalassiosira pseudonana, and the cyanobacterium Synechococcus sp. Linear discriminant analysis (LDA) was used to determine a suitable set of linear discriminant functions for classification of these species over an optimal wavelength range. Multivariate optical elements (MOEs) were then designed to predict the linear discriminant scores for the same calibration spectra. The theoretical performance specifications of these MOEs are described.

A new Matlab-based software suite called Tilt-A-Whirl has been applied to XRD data from textured gold films electro-deposited onto nickel substrates. The software routines facilitate phase identification, texture analysis via pole figure visualization, and macrostrain determination. The use of principal component analysis with multivariate curve resolution (PCA/MCR) revealed the extraction of texture components. The unusual hardness properties of one Au film (deposited from a 30% gold depleted BDT-200 bath) were found to be dependent on the (210) out-of-plane preferred orientation of the polycrystalline gold film. The progressive nucleation of Au crystallites during electro-plating has been tied to improved hardness properties of this film.

We describe the automatic analysis of fluorescence tracks of phytoplankton recorded with a fluorescence imaging photometer. The optical components and construction of the photometer were described in Part I and Part II of this series in this issue. An algorithm first isolates tracks corresponding to a single phytoplankter transit in the nominal focal plane of a flow cell. Then, the fluorescence streaks in the track that correspond to individual optical elements on the filter wheel are identified. The fluorescence intensity of each streak is integrated and used to calculate ratios. This approach was tested using 853 fluorescence measurements of the coccolithophore Emiliania huxleyi and the diatom Thalassiosira pseudonana. Average intensity ratios for the two classes closely follow those predicted in Part I of this series, with a distribution of ratios in each class that is consistent with the signal-to-noise ratio calculations in Part II for single cells. No overlap of the two class ratios was observed, yielding perfect classification.

Multivariate optical computing (MOC) is a compressed sensing technique with the ability to provide accurate spectroscopic compositional analysis in a variety of different applications to multiple industries. Indeed, recent developments have demonstrated the successful deployment of MOC sensors in downhole/well-logging environments to interrogate the composition of hydrocarbon and other chemical constituents in oil and gas reservoirs. However, new challenges have necessitated sensors that operate at high temperatures and pressures (up to 230°C and 138 MPa) as well as even smaller areas that require the miniaturization of their physical footprint. To this end, this paper details the design, fabrication, and testing of a novel miniature-sized MOC sensor suited for harsh environments. A micrometer-sized optical element provides the active spectroscopic analysis. The resulting MOC sensor is no larger than two standard AAA batteries yet is capable of operating in high temperature and pressure conditions while providing accurate spectroscopic compositional analysis comparable to a laboratory Fourier transform infrared spectrometer.

Polymer films of varying thicknesses were deposited onto cotton and polyester fabric samples by dip-coating from solution. Scanning electron microscopy (SEM) images of the coated fabric samples were used to evaluate the quality of the polymer coating. The samples were analyzed by infrared diffuse reflection spectroscopy to determine the relationship between film thickness and the effect of the coating on the spectroscopy of the two fabrics. Effects observed in four limiting cases are examined: (Case I) weak coating absorption on a fabric with weak absorption at the same frequency; (Case II) strong coating absorption in a spectral region of weak fabric absorption; (Case III) weak coating absorption in a spectral region of strong fabric absorption; and (Case IV) strong coating absorption in a spectral region of strong fabric absorption. In the first case, effects were dominated by reduced scattering as the coating is added. In the second case, the strong coating absorption that was observed at low coverages plateaus at higher coverage due to depth of penetration effects. In the third and fourth cases, reduced Fresnel diffuse reflection is measured as the coating is added, consistent with the reduction of scattering observed in the first case.

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Viscosity is driven by asphaltene content and is a key parameter in the development of heavy oil fields. Understanding fluid composition and temperature and pressure-induced changes in fluid viscosity is vital for an optimized production strategy and surface facility design. A recent field and laboratory study exemplifies the steps necessary to obtain the fit-for-purpose data from heavy oil samples. This paper presents the case study of a new downhole optical composition analysis sensor used during real-time downhole fluid analysis and sampling for the first time in a Kuwait heavy oil formation. The primary objectives of a sampling program are to confirm fluid indications on the openhole logs and collect crucial pressure/volume/temperature (PVT) samples. The downhole optical composition analysis sensor provides the information necessary to estimate a sample contamination level. It also indicates when the sample is sufficiently clean for PVT analysis. The samples should be acquired from the reservoir and maintained as single phase throughout transport to the laboratory. The pressure should be maintained higher than the asphaltene precipitation onset pressure and much higher than the bubblepoint. If the sample is not maintained higher than the asphaltene onset pressure, asphaltenes precipitate in the sample chamber and cannot be reconstituted as single phase in the laboratory. The new optical composition analyzer can also identify fluid stream components and their relative concentration in real time with laboratory-quality accuracy downhole. Near-infrared (NIR) sensors are most commonly used to identify fluid in the wireline formation tester (WFT). The sensors work well in light hydrocarbons. However, in heavy oil, the sensor performance degrades and fails to identify the contamination level accurately. The new multivariate optical computing (MOC) technique for downhole optical composition analysis overcomes this by performing a photometric detection with the entire relevant spectral range compared to spectroscopic analysis, which is only performed over a narrow band or sparse set of channels while traditional sensors are configured. The MOC sensor also recognizes in real time the chemical nature (optical fingerprint) of analytes (e.g., methane, ethane, propane, carbon dioxide, hydrogen sulfide, water, asphaltene, aromatics, and saturates) using all of the useful information in the optical spectrum. The real-time analyte chemical composition provided by the sensor is comparable to laboratory tests conducted on the collected PVT sample. Laboratory measurements on representative fluid samples from the correct locations early in the field development stage help develop an optimal field-development strategy. At the same time, sample integrity is maintained from the reservoir to the laboratory, which is vital. This paper discusses how the new optical compositional analysis sensor in combination with a high-resolution fluid identification sensor provides comprehensive and accurate downhole fluid composition in real time. This compares well with the laboratory-measured PVT analysis of heavy oil samples. The compositional analysis sensor optimizes pumpout time, thus helping obtain practically ideal contamination levels to begin the single-phase sampling process, which saves valuable rig time.