The accurate determination of the diffusion coefficient of dissolved CO2 in aqueous solutions is a key parameter to predict the transport of the dissolved gas in the aqueous solution of porous aquifers and caprocks in the context of the mitigation of greenhouse gases emissions. A better understanding of the diffusion kinetics of dissolved CO2 as a function of subsurface conditions is essential to ensure the integrity of CO2 storage in saline aquifers. Experimental data are available to predict the CO2 diffusion kinetics as a function of temperature and pressure. However, the impact of salinity on CO2 diffusivity is still poorly documented. In this work, the diffusion coefficients of dissolved CO2 were determined using in situ Raman microspectrometry in Fused Silica Capillaries in a range of salinity from 0.0 to 6.0 molNaCl kg−1H2O. The diffusion coefficient of CO2 dissolved in pure water at 21 ± 1 °C and 40 bar is 1.71 × 10−9 ± 0.06 m2 s−1. In the same conditions of pressure and temperature, the diffusion coefficient of dissolved CO2 decreases with salinity increase: from 0.0 to 6.0 molNaCl kg−1H2O (the diffusion kinetics of dissolved CO2 is divided by two). Consequently, the impact of salinity on the diffusion coefficient of dissolved CO2 must be taken into account to evaluate the transport of dissolved gases in the brine of the reservoir and caprock. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Geophysical imaging using the inversion procedure is a powerful tool for the exploration of the Earth's subsurface. However, the interpretation of inverted images can sometimes be difficult, due to the inherent limitations of existing inversion algorithms, which produce smoothed sections. In order to improve and automate the processing and interpretation of inverted geophysical models, we propose an approach inspired from data mining. We selected an algorithm known as DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to perform clustering of inverted geophysical sections. The methodology relies on the automatic sorting and clustering of data. DBSCAN detects clusters in the inverted electrical resistivity values, with no prior knowledge of the number of clusters. This algorithm has the advantage of being defined by only two parameters: the neighbourhood of a point in the data space, and the minimum number of data points in this neighbourhood. We propose an objective procedure for the determination of these two parameters. The proof of concept described here is applied to simulated ERT (Electrical Resistivity Tomography) sections, for the following three cases: two layers with a step, two layers with a rebound, and two layers with an anomaly embedded in the upper layer. To validate this approach, sensitivity studies were carried out on both of the above parameters, as well as to assess the influence of noise on the algorithm's performance. Finally, this methodology was tested on real field data. DBSCAN detects clusters in the inverted electrical resistivity models, and the former are then associated with various types of earth materials, thus allowing the structure of the prospected area to be determined. The proposed data-mining algorithm is shown to be effective, and to improve the interpretation of the inverted ERT sections. This new approach has considerable potential, as it can be applied to any geophysical data represented in the form of sections or maps.

This study proposes a method to select a wavelet basis for classification. It uses a strategy defined by Wickerhauser and Coifman and proposes a new additive criterion describing the contrast between classes. Its performance is compared with other approaches on simulated signals and on experimental EEG signals for brain-computer interface applications.

Concrete/Steel/concrete pipes are present in French nuclear powerplants. These pipelines are composed of three layers: an internal layer of centimeter concrete, a millimeter layer of steel and a layer of centimeter reinforced concrete. The pipelines located by the sea undergo corrosion of the steel part caused by the chlorides present in the seawater they transport. Nowadays, the diagnosis of corrosion is made with electrochemical measurements. But they provide a qualitative indicator about corrosion. In fact, the goal is to dispose of a Non-Destructive Technique capable of evaluating the extension of holes in the steel part with a minimum diameter of 1 cm and the steel thickness elsewhere with a 100 µm uncertainty. The inspection must be conducted from the outside of the pipe still working. Ultrasonic bulk waves, ultrasonic guided waves and vibration techniques are investigated. All of them are studied with a methodology from the lab to the field. In lab, specimens are manufactured and tested. The experimental results are compared to numerical results obtained with simulation tools like CIVA or Specfem2D. Then, the promising techniques are tested on a real concrete/steel/concrete pipe. For the bulk wave techniques, the major issue is to increase the frequency signal over 1 MHz to get a better resolution of the steel investigation. But at this frequency range, the multi-scattering phenomenon on concrete aggregates is prominent. First experimental results using pulse compression on lab samples are promising: holes in steel give a specific signature and compression techniques are used to get a better resolution in steel thickness after signal autocorrelation. For both guided wave and vibration techniques, simulations are used to determine modes sensitive to the steel thickness and Zero Group Velocity modes which will induce a strong characteristic frequency response. Experimental study is going on to reveal these modes on lab specimen.

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