In recent years, optical computing devices have been developed for chemical sensing applications including, but not limited to, those in the oil and gas industry in the form of optical sensors on downhole or surface equipment to evaluate a variety of reservoir fluid properties. In general, an optical computing device is a device configured to receive an input of electromagnetic radiation from a sample and produce an output of electromagnetic radiation from a processing element, wherein measured integrated intensity of the electromagnetic radiation from the processing element is related to a component or components within the sample. The optical computing device may be, for example, an Integrated Computational Element (“ICE”). One type of an ICE is a multi-layered optical thin film optical interference device, also known as a multivariate optical element (“MOE”).
Fundamentally, optical computing devices utilize optical elements to perform calculations, as opposed to the hardwired circuits of conventional electronic processors. When light from a light source interacts with a substance, unique physical and chemical information about the substance is encoded in the electromagnetic radiation that is reflected from, transmitted through, or radiated from the sample. Thus, the optical computing device, for example through use of the ICE and one or more detectors, is capable of extracting the information of one or multiple characteristics/analytes within a substance and converting that information into a detectable output signal reflecting the overall properties of a sample. Such characteristics may include, for example, the presence of certain elements, compositions, fluid phases, etc. existing within the substance.
Traditional ICEs include pluralities of optical thin film layers consisting of various materials whose complex indices of refraction and size (e.g., thickness) varies between each layer. A traditional ICE design refers to the substrate, number and thicknesses of the respective layers of the traditional ICE, and the refractive indices of the substrate and layers. The layers may be strategically deposited and sized so as to selectively pass predetermined fractions of electromagnetic radiation at different wavelengths configured to substantially mimic a regression vector corresponding to a particular physical or chemical characteristic of interest of a substance of interest. Accordingly, a traditional ICE design will exhibit a transmission function that is weighted with respect to wavelength. As a result, the output light intensity from the ICE conveyed to the detector may be related to the physical or chemical characteristic of interest for the substance.
Currently, ICE designs are assessed by applying an ICE regression vector to a single set of calibration data (i.e., spectral data set) to evaluate a performance factor, for example but not limited to, a standard error of calibration (“SEC”). This procedure is performed on a set of spectral data that describes a single chemical system that contains one or more components: its target characteristic and the remaining components (including spectral interferents), usually referred to the matrix. A subset of the chemical system can be used for validation purposes to calculate the performance factor, for example, the standard error of prediction; and represents the same chemical system as the calibration set. An illustrative ICE can be constructed as a series of alternating layers of high and low refractive index materials with associated thicknesses deposited onto an optical substrate. Such a device has an optical transmission function (T), designed by assessing a performance factor (e.g. SEC) and using a minimization function to adjust the layer thicknesses to design an ICE with an optimal performance factor (e.g. low SEC), which is thus as predictive as possible. As a result, the design and fabrication of multi-layered thin film ICEs can be very time-consuming and costly.
Accordingly, there is a need in the art for a more cost-effective approach to multivariate optical computing.