Date of Award

2017

Document Type

Open Access Dissertation

Department

Chemistry and Biochemistry

Sub-Department

College of Arts and Sciences

First Advisor

Michael L. Myrick

Abstract

Multivariate optical computing (MOC) is a compressive sensing technique for which an analyte concentration is detected in an interfering mixture by direct detector output. The detector measures the dot product of a linear regression vector with a sample spectrum, as an analog optical computation. The computation is accomplished with the multivariate optical element (MOE), to which the optical regression vector is encoded as a transmission pattern. As a spectrum of light emanates from a sample and passes through the MOE, the dot product naturally occurs when light strikes the detector. The MOC platform allows a simple, robust, and direct measurement of chemical properties. This work extends the MOC platform to high temperature, high pressure harsh environments and is tested with petroleum fluids in-situ within subterranean petroleum wells.

This work describes a unique experimental apparatus and method necessary to gather petroleum fluid reference spectra for petroleum at reservoir conditions. The instrument is capable of measuring the optical spectrum (long-wave ultraviolet through short-wave mid-infrared) of fluids from ambient up to 138 MPa (20,000 psia) and 422 K (300°F) using ~5 mL of fluid. The instrument is validated with ethane.

This work further describes new design and fabrication techniques necessary to enable a harsh environment single-core MOE. The entirely new MOE fabrication technology uses a highly customized ion-assisted electron-beam (e-beam) deposition system, with new processes control techniques. For methane, an analyte with relatively low interference, the MOC sensor validates within 1% relative accuracy of a laboratory Fourier transform infrared (FTIR) spectrometer using partial least squares (PLS) regression.

Lastly this work describes a new MOC dual-core configuration, which is able to better mimic complex regression vector behavior relative to a single-core, thus enabling better analysis for analytes more highly interfered by complex petroleum fluid background. The regression vector is encoded as the linear combination of two MOE transmission patterns. Design considerations, the design workflow and fabrication methodology are described. High temperature and pressure laboratory and field validation is presented for methane with the single-core MOC sensor and methane and carbon dioxide for the dual-core MOC sensor.

Rights

© 2017, Christopher Michael Jones

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