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Norway is one of the major suppliers of oil and gas to the world market. Revenues from sales of oil and gas have played an important role in building modern Norwegian society. Oil and gas are trapped in the subsurface formation of relatively thin slabs of porous rock. The oil wells are drilled into the subsurface with an oil rig to extract the oil and gas from the reservoir. The production of oil can be increased by predicting and managing the future performance of the oil reservoir. However, because of the subsurface complexity and limited data, numerous uncertainties are present in oil reservoir characterization. These uncertainties should be considered for better future prediction of oil reservoir performance. In this paper, a simplified 2D control relevant model for a slightly slanting wedge-shaped black oil reservoir as in Zhang (2013) is made more realistic by incorporating model uncertainty. Furthermore, based on this model with uncertainty, a Proportional Integral (PI) controller is implemented to increase the oil production while minimizing the water production. The model in Zhang (2013) is re-implemented in Julia programming language. A standard package called DifferentialEquations.jl, available in Julia, is used to solve the model. A Tsit5() solver under this package solves the ODE problems with variable time steps which reduce the computational time and improve the performance. Furthermore, the uncertainty in the model is predicted via a parallel Monte Carlo simulation. A parallel Monte Carlo simulation is performed using EnsembleProblem in Julia. Finally, a PI controller is implemented in the model with uncertainty to control the valve opening of the Inlet Control Valves (ICVs) in the production well. The implementation of a PI controller improved the total oil production in 1000 days by 1.7873%. However, the effect is not very significant due to the limited capability of a PI controller. In this case, a more effective controller, such as a model predictive controller (MPC) is required.

Plasmon-enhanced chemical transformations at the solid-liquid interface can be imaged with high sensitivity, chemical selectivity, and nanoscale precision through tip-enhanced Raman scattering (TERS). We demonstrate the latter for the first time through measurements aimed at (i) locating plasmonic hotspots at the solid-liquid interface at which chemical transformations take place, (ii) monitoring the evolution from reactants to products through their distinct Raman spectra, and (iii) 2D correlation analysis of Raman time trajectories to unambiguously extract the spectral components that mark chemical transformation and to understand the correlations between the product and parent signatures. For our proof-of-principle study, we select a model plasmon-enhanced chemical process, namely, the dimerization of p-nitrothiophenol to dimercaptoazobenzene, but now, at the solid-liquid interface. Our plasmonic construct otherwise consists of chemically functionalized gold microplates in aqueous solution, which we image using a gold-coated TERS probe irradiated at 633-nm. Overall, we demonstrate chemical reaction imaging in aqueous solution via TERS for the first time, herein, at a pixel-limited lateral spatial resolution of 10 nm.

We demonstrate fast (0.25 s/pixel) nanoscale chemical imaging in aqueous solution via tip-enhanced Raman scattering (TERS), with sub-15 nm spatial resolution. The substrate consists of 4-mercaptobenzonitrile-functionalized monocrystalline gold triangular platelets immersed in H2O, which we map using a gold-coated AFM probe illuminated using a 633 nm laser source. We find that the recorded TERS images trace the enhanced local optical fields that are sustained towards the edges and corners of the triangles. In effect, we directly map local optical fields of a plasmonic substrate in aqueous solution through molecular TERS. Overall, our described platform and approach may generally be used for chemical and biological imaging, and potentially, to follow chemical transformations at solid-liquid interfaces through TERS.

We describe a simplified approach to simulating Raman spectra using ab initio molecular dynamics (AIMD) calculations. Our protocol relies on on-the-fly calculations of approximate molecular polarizabilities using a sum over orbitals (as opposed to states) method.