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Quantifying and minimizing uncertainty is vital for simulating technically and economically successful geothermal reservoirs. To this end, we apply a stochastic modelling sequence, a Monte Carlo study, based on (i) creating an ensemble of possible realizations of a reservoir model, (ii) forward simulation of fluid flow and heat transport, and (iii) constraining post-processing using observed state variables. To generate the ensemble, we use the stochastic algorithm of Sequential Gaussian Simulation and test its potential fitting rock properties, such as thermal conductivity and permeability, of a synthetic reference model and—performing a corresponding forward simulation—state variables such as temperature. The ensemble yields probability distributions of rock properties and state variables at any location inside the reservoir. In addition, we perform a constraining post-processing in order to minimize the uncertainty of the obtained distributions by conditioning the ensemble to observed state variables, in this case temperature. This constraining post-processing works particularly well on systems dominated by fluid flow. The stochastic modelling sequence is applied to a large, steady-state 3-D heat flow model of a reservoir in The Hague, Netherlands. The spatial thermal conductivity distribution is simulated stochastically based on available logging data. Errors of bottom-hole temperatures provide thresholds for the constraining technique performed afterwards. This reduce the temperature uncertainty for the proposed target location significantly from 25 to 12 K (full distribution width) in a depth of 2300 m. Assuming a Gaussian shape of the temperature distribution, the standard deviation is 1.8 K. To allow a more comprehensive approach to quantify uncertainty, we also implement the stochastic simulation of boundary conditions and demonstrate this for the basal specific heat flow in the reservoir of The Hague. As expected, this results in a larger distribution width and hence, a larger, but more realistic uncertainty estimate. However, applying the constraining post-processing the uncertainty is again reduced to the level of the post-processing without stochastic boundary simulation. Thus, constraining post-processing is a suitable tool for reducing uncertainty estimates by observed state variables.
Capacitive field-effect electrolyte-insulator-semiconductor sensors consisting of an Al-p-Si-SiO2 structure have been used for the electrical detection of unlabelled single- and double-stranded DNA (dsDNA) molecules by their intrinsic charge. A simple functionalization protocol based on the layer-by-layer (LbL) technique was used to prepare a weak polyelectrolyte/probe-DNA bilayer, followed by the hybridization with complementary target DNA molecules. Due to the flat orientation of the LbL-adsorbed DNA molecules, a high sensor signal has been achieved. In addition, direct label-free detection of in-solution hybridized dsDNA molecules has been studied.
The chemical imaging sensor is a field-effect sensor which is able to visualize both the distribution of ions (in LAPS mode) and the distribution of impedance (in SPIM mode) inthe sample. In this study, a novel wound-healing assay is proposed, in which the chemical imaging sensor operated in SPIM mode is applied to monitor the defect of a cell layer brought into proximity of the sensing surface.A reduced impedance inside the defect, which was artificially formed ina cell layer, was successfully visualized in a photocurrent image.
A sensor system for investigating (bio)degradationprocesses of polymers is presented. The system utilizes semiconductor field-effect sensors and is capable of monitoring the degradation process in-situ and in real-time. The degradation of the polymer poly(d,l-lactic acid) is exemplarily monitored in solutions with different pH value, pH-buffer solution containing the model enzyme lipase from Rhizomucormiehei and cell-culture medium containing supernatants from stimulated and non-stimulated THP-1-derived macrophages mimicking activation of the immune system.
An amperometric enzyme biosensor has been applied for the detection of adrenaline. The adrenaline biosensor has been prepared by modification of an oxygen electrode with the enzyme laccase that operates at a broad pH range between pH 3.5 to pH 8. The enzyme molecules were immobilized via cross-linking with glutaraldehyde. The sensitivity of the developed adrenaline biosensor in different pH buffer solutions has been studied.