Abstract. Surface plasmon resonance (SPR) chips are widely used to measure association and dissociation rates for the binding kinetics between two species of chemicals, e.g., cell receptors and ligands. It is commonly assumed that ligands are spatially well mixed in the SPR region, and hence a mean-field rate equation description is appropriate. This approximation however ignores the spatial fluctuations as well as temporal correlations induced by multiple local rebinding events, which become prominent for slow diffusion rates and high binding affinities. We report detailed Monte Carlo simulations of ligand binding kinetics in an SPR cell subject to laminar flow. We extract the binding and dissociation rates by means of the techniques frequently employed in experimental analysis that are motivated by the mean-field approximation. We find major discrepancies in a wide parameter regime between the thus extracted rates and the known input simulation values. These results underscore the crucial quantitative importance of spatio-temporal correlations in binary reaction kinetics in SPR cell geometries, and demonstrate the failure of a mean-field analysis of SPR cells in the regime of high Damköhler number Da > 0.1, where the spatio-temporal correlations due to diffusive transport and ligand-receptor rebinding events dominate the dynamics of SPR systems.

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The distributed nature of fiber-optic measurements such as distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and distributed strain sensing (DSS) enables nearly continuous monitoring of the downhole environment in both space and time. Though continuous monitoring opens the door to a rich new set of asset management applications, it comes with its own set of challenges in terms of data transmission, management, and security. Recently, cloud-based fiber-optic data management services have been successfully introduced to the oil and gas industry as an effective way to collect, transfer, store and display distributed measurement data from the downhole environment. To maximize the value of such cloud-based data management systems, and further improve the return on investment for asset managers, the large volume of distributed sensing data collected must be converted to value in a simple and easy-to-use form, depending on different applications. Traditionally, interpretation of distributed sensing data is a manual process conducted by engineers in a post-job workflow. This paper presents the successful integration of an analytics library into the cloud-based fiber-optic data management system. This integration enables real-time, and in some cases near real-time, asset decision making. The design of the analytics architecture is open to meet the wide range of application requirements by asset managers. A few application examples of the analytics integration will be presented using real-time data streamed directly from the field.

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