Eni (France)

The analysis of environmental DNA (eDNA) allows efficient surveys of freshwater species and is being increasingly used. However, most studies generally have a limited sampling plan. Because different methods are used by different authors, all the data produced with eDNA cannot be easily pooled together for a global overview. Nevertheless, one of the promising perspectives is the standardization of the methods and protocols, for long-term monitoring, early detection of invasive species, and rare species detection. We here present the biggest dataset using eDNA metabarcoding with standardized methods for freshwater bivalves. Sampling was performed mainly in France, with over 350 localities spread over all major basins. Metabarcoding was performed using two new sets of primers, one for the Unionida and one for the Venerida. The species distributions inferred from eDNA sampling are then compared with those currently known. Results give an insight into the valuable data that eDNA analysis can provide if used at a large scale. Some species were found outside of their known range, especially invasive species but also species of conservation interest. Others were found in a surprisingly low number of localities, although currently considered widespread and of no conservation interest. This can be explained by the databases currently used to establish conservation status, which are often biased by including ancient data, shell-only data, and potentially misidentified species. The present study shows that our eDNA metabarcoding approach will be an essential tool to unveil the true species distributions, to better evaluate their status and improve conservation policymaking.

Renewable Energies (RES) penetration is progressing rapidly: in France, the installed capacity of photovoltaic (PV) power rose from 26MW in 2007 to 8GW in 2017 [1]. Power generated by PV plants being highly dependent on variable weather conditions, this ever-growing pace is raising issues regarding grid stability and revenue optimization. To overcome these obstacles, PV forecasting became an area of intense research. In this paper, we propose a low complexity forecasting model able to operate with multiple heterogenous sources of data (power measurements, satellite images and Numerical Weather Predictions (NWP)). Being non-parametric, this model can be extended to include inputs. The main strength of the proposed model lies in its ability to automatically select the optimal sources of data according to the desired forecast horizon (from 15min to 6h ahead) thanks to a feature selection procedure. To take advantage of the growing number of PV plants, a Spatio-Temporal (ST) approach is implemented. This approach considers the dependencies between spatially distributed plants. Each source has been studied incrementally so as to quantify their impact on forecast performances. This plurality of sources enhances the forecasting performances up to 40% in terms of RMSE compared to a reference model. The evaluation process is carried out on nine PV plants from the Compagnie Nationale du Rhone (CNR).

La Compagnie nationale du Rhône (CNR) a été confrontée dans le cadre des nombreuses opérations de restauration des milieux aquatiques qu'elle mène, au manque de filières de traitement pour les plantes exotiques envahissantes et en particulier concernant les terres colonisées par des rhizomes de renouées du Japon sur des gros volumes. Depuis 2011, la CNR a donc décidé de développer en interne diverses méthodes de traitement ayant pour objectif la dévitalisation immédiate des rhizomes de la plante dans les déblais de terrassement. Après deux ans de tests, et trois ans d'application en interne des méthodes expérimentées, les résultats sont très satisfaisants et une technique particulièrement efficace a pu être proposée et déployée sur des opérations de restauration de grande échelle suivies par la CNR. La méthode en question est constituée de deux étapes clefs : une première de criblage pour séparer les éléments fins des éléments grossiers présents dans les matériaux alluviaux, la seconde pour dévitaliser les rhizomes présents dans la fraction grossière par concassage fin. Les matériaux traités peuvent ainsi être remis au cours d'eau, valorisés comme substrat de plantation ou évacués dans une installation de stockage de déchets inertes (ex « décharge de classe 3 »).

Abstract An historic pact on Climate Change signed by 195 countries during the last COP 21 in Paris 2015 established elements to deal with emission reductions and global temperature rise, which will take effect by 2020. Therefore, renewable energy sources are essential to reach these new targets, like the world area's abundant solar resources that can be developed in order to contribute partially to the fossil fuel reduction, increasing the energy efficiency of O&G plants and reducing the emissions level at the same time. Solar PV (photovoltaic) is the most widely used solar energy technology to date and secondly, CSP (Concentrated Solar Power). While PV converts sunlight directly into electricity by using panel arrays based on semiconductor materials, the CSP make use of mirrors to concentrate the sun's energy and produce steam that can be used in the oil and gas industry in different ways: To drive steam turbines and produce the electrical power to supplement the oil and gas facilities demand.For thermal technologies in enhanced oil recovery systems or generally for plant process units steam use. All of the aforementioned CSP applications have the possibility to add thermal energy storage systems needed to overcome night time or for topping off during non-ideal solar conditions. Nowadays the photovoltaic cells have evolved technologically being very affordable with decreasing price trends and an increasing interest in standalone application at medium to large-scale capacity, nevertheless the CSP is shyly appearing in the oil and gas industry in specific geographical areas basing its attractiveness as a source of steam generation. The present paper refers to a case study of a combined-cycle power plant which benefits of the integration with a solar field (Integrated Solar Combined Cycle – ISCC) for oil and gas production facilities use sited in the North Africa region. This geographical area offers appropriate solar DNI (Direct Normal Irradiance) for CSP technologies. The selected configuration for this case, exploit the CSP heat in the same heat recovery steam generator (HRSG) of the combined cycle. Solar heat is integrated at the evaporation section, with an efficient heat exchange coming from the CSP system. In this study, it is presented a fuel saving configuration, in which constant power is produced. Therefore gas turbine electric load decreases up to 80% and the remaining electricity is produced in the steam cycle exploiting the heat coming from the solar field, which thermal load is decided as an overall compromise solution for the CSP field size. Results from the life cycle cost analysis showed that investment payback in order to be inside project operational life limits is largely driven from two market variables remaining a barrier: fuel gas price and CO2 shadow price; other limitation that might be an issue is water scarcity in the area for steam cycle or make up and also any permitting for connection to the grid unless the plant will be operated standalone.

A strategy to establish a model that takes into account the different pitfalls relative to the simulation of ZnO nanowire (NW) based devices is proposed. Starting from the governing equations of the problem, an equivalent circuit of a ZnO NW working in quasi-static 33 mode is established. It takes into account the semiconducting properties of the NW and the effect of the Schottky contact between the tip of the NW and an electrode. Such a circuit model is expected to become a valuable tool to evaluate the performances of NWs as active elements in integrated systems. As an application, a microgenerator based on an NW array under a compressive strain has been modelled. The influence of the electrode material and the NW dimensions on the microgenerator performances is investigated. It is shown that the NW aspect ratio is of critical importance to achieve a high efficient energy conversion.

Surface properties of a modified single-crystal silver (111) face are studied in aqueous 1 M NaNO3 solutions with different acidity in the presence of a monolayer n-decanethiol (DT) film by the methods of electrochemical impedance spectroscopy (EIS) and cyclic voltammetry that employs the meniscus contact technique. It is shown experimentally that in the potential range from 0 to −0.5 V, a DT-modified silver electrode in NaNO3 solutions with pH 6 behaves as an ideal capacitance in a wide frequency range (from 0.01 Hz to 100 kHz). It is found that with the increase in the solution acidity up to pH 4 and 2, the region of stable capacitance characteristics narrows down. The concomitant changes in the adsorbed monolayer structure lead to deviations of impedance characteristics measured using a low-frequency ac signal from those of the ideal capacitance. A potential shift to values more negative than −0.5 V leads to the destruction of the monolayer. Based on the analysis of experimental EIS characteristics, system’s deviation from the ideal behavior that may indicate violation of the layer continuity is assessed and the relaxation frequencies of the DT/Ag(111) interface in solutions of different acidity are calculated. Estimates of capacitance, resistance, and thickness of the adsorbed monolayer are obtained as a function of the solution pH.

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Summary Reservoir management is based on the prediction of reservoir performance by means of numerical-simulation models. Reliable predictions require that the numerical model mimic the production history. Therefore, the numerical model is modified to match the production data. This process is termed history matching (HM). Form a mathematical viewpoint, HM is an optimization problem, where the target is to minimize an objective function quantifying the misfit between observed and simulated production data. One of the main problems in HM is the choice of an effective parameterization—a set of reservoir properties that can be plausibly altered to get a history-matched model. This issue is known as a parameter-identification problem, and its solution usually represents a significant step in HM projects. In this paper, we propose a practical implementation of a multiscale approach aimed at identifying effective parameterizations in real-life HM problems. The approach requires the availability of gradient simulators capable of providing the user with derivatives of the objective function with respect to the parameters at hand. Objective-function derivatives can then be used in a multiscale setting to define a sequence of richer and richer parameterizations. At each step of the sequence, the matching of the production data is improved by means of a gradient-based optimization. The methodology was validated on a synthetic case and was applied to history match the simulation model of a North Sea oil reservoir. The proposed methodology can be considered a practical solution for parameter-identification problems in many real cases until sound methodologies (primarily adaptive multiscale estimation of parameters) become available in commercial software programs.