Highlights• T2Well-EWASG, a coupled wellbore-reservoir numerical simulator for geothermal systems.• Interpretation of geothermal well-tests.• Application of T2Well-EWASG on a short well-test performed on a well of the Wotten Waven geothermal field (Dominica). AbstractIn the geothermal sector, being able to simulate production tests by combining surface and downhole measurements can be extremely useful, improving data interpretation and reducing the impact of unavailable field data. This is possible with T2Well, a coupled wellbore-reservoir simulator. We plugged the EWASG equation of state for high enthalpy geothermal reservoirs into T2Well and extended the function to analytically compute the heat exchange between wellbore and formation at the short times. Changes to the analytical heat exchange function were verified by comparison with wellbore-formation heat exchange numerically simulated. T2Well-EWASG was validated by reproducing the flowing pressure and temperature logs taken from literature, and by using the software for the interpretation of a short production test. Simulation results indicate that T2Well-EWASG can be effectively used to improve the interpretation of production tests performed in geothermal wells.

Dissolution dynamic nuclear polarization allows in vivo studies of metabolic flux using 13 C-hyperpolarized tracers by enhancing signal intensity by up to four orders of magnitude. The T 1 for in vivo applications is typically in the range of 10-50 s for the different 13 C-enriched metabolic substrates; the exponential loss of polarization due to various relaxation mechanisms leads to a strong reduction of the signal-to-noise ratio (SNR). A common solution to the problem of low SNR is the accumulation/averaging of consecutive spectra. However, some limitations related to long delays between consecutive scans occur: in particular, following biochemical kinetics and estimate apparent enzymatic constants becomes time critical when measurement scans are repeated with the typical delay of about 3 T 1 . Here we propose a method to dramatically reduce the noise, and therefore also the acquisition times, by computing, via truncated singular value decomposition, a low-rank approximation to the individual complex time-domain signals. Moreover, this approach has the additional advantage that the phase correction can be applied to the spectra already denoised, thus greatly reducing phase correction errors. We have tested the method on (1) simulated data;(2) performing dissolution of hyperpolarized 1-13 C-pyruvate in standard conditions and (3) in vivo data sets, using a porcine model injected with hyperpolarized Na-1-13 C-acetate. It was shown that the presented method reduces the noise level in all the experimental data sets, allowing the retrieval of signals from highly noisy data without any prior phase correction pre-processing. The effects of the proposed approach on the quantification of metabolic kinetics parameters have to be shown by full quantification studies.

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TMGAS, an equation of state module of TOUGH2 V.2.0 reservoir simulator, was used to model the migration of CO2, H2S, and CH4 in a deep sedimentary formation. The scope is the improvement of the description of non‐condensable gas (NCG) migration within modeling studies of sedimentary basins’ evolution. Different scenarios have been simulated with NCG migration taking place in a large sedimentary formation discretized with a full 3D Voronoi approach by using specifically improved versions of the pre‐ and post‐processing tools for TOUGH2 developed at the University of Bologna. Simulated reference scenarios are related to the migration of CO2, H2S, and CH4 generated at constant rate for 1 × 106 years in a fresh water aquifer. Additional scenarios are simulated with NCG migration taking place in the same formation but saturated with brine. The effects of pressure‐temperature‐composition (PTX) conditions on thermodynamic equilibria, phase composition, phase thermo‐physical properties and, consequently, on the migration features of different NCGs are modeled and discussed. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd

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In order to well distinguish different tissues of the human body by magnetic resonance imaging (MRI), it is of great importance to find procedures to improve the image contrast. In particular, a valuable feature is to image only specific parts of organs and/or tissues while ignoring all the others. Dedicated MRI sequences able to filter the 1 H nuclei signals based on the different longitudinal relaxation times (T1) of the tissues have been developed. Standard signal selection/attenuation sequences, such as the Short Time Inversion Recovery and Multiple Inversion Recovery, have the effect to zero the signal for a discrete number of T1 values. Parametrically Enabled Relaxation Filters with Double and multiple Inversion (PERFIDI) sequences act on a range of T1 values and behave as an electronic band-pass or high-pass or low-pass filters. PERFIDI filters are therefore primarily focused on the components that pass through, rather than on those that are blocked. These filters have been developed and tested by nuclear magnetic resonance relaxometry. Here, these sequences have been validated for MRI on phantom samples to mimic T1 distributions present in tissues. Preliminary applications show that PERFIDI filters can effectively work on a range of T1 values to give well contrasted images.

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In many branches of physics, the time evolution of various quantities measured in systems passing from excited to equilibrium states, while theoretically very complex, can be in practice well approximated by a sum of exponential decays.Multiexponential relaxometry data analysis is about determining the number of exponential components and their corresponding amplitudes and decay rates, starting from noisy recorded time series, under the assumption of the discreteness of the number of components present. A technique for decomposing a signal modelled as a sum of exponential decays into its components is introduced, consisting of a modified version of the algorithm minimum description length (MDL) + matrix pencil, originally proposed by Lin et al. for the analysis of nuclear magnetic resonance spectroscopy data. The procedure starts by denoising the discrete time-domain signal, and then a number of different decimations are applied, each being followed by an MDL + matrix pencil detection-estimation step, and finally, a postprocessing of the intermediate outcomes is done. The comprised model order estimator eliminates the need of providing prior estimates of the number of components present.