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Summary Sedimentary rocks display complex spatial distribution of both pore space and solid components, impacting the directional dependence of physical phenomena such as electrical conduction, fluid flow, heat transfer, and molecular diffusion. The complexity of the pore space is often quantified by the concept of tortuosity, which measures the sinuosity of the connecting paths in the pore space. Tortuosity is an important quantity in formation evaluation as it impacts petrophysical properties such as permeability and formation factor. However, the existence of various techniques can lead to nonuniqueness in assessment of tortuosity. Furthermore, spatial variation of the solid components of the rocks occurring at the core-scale domain reflected in the connectivity and distribution of the minerals is typically not quantified. The objectives of this paper are (a) to quantify tortuosity and tortuosity anisotropy of porous media through estimation of electrical, diffusional, hydraulic, and geometrical tortuosity at the pore scale and core scale and (b) to compare electrical, diffusional, hydraulic, and geometrical tortuosity. We estimate tortuosity in the pore space of microcomputed tomography (micro-CT) scan images and in the most connected and abundant solid phase of whole-core CT scan images. We conduct numerical simulations of electric potential distribution, diffusion, and fluid flow and velocity distribution to estimate electrical, diffusional, and hydraulic tortuosity, respectively. To calculate geometrical tortuosity, we use the segmented pore space from micro-CT scan images to extract a pore network model and compute the shortest path of all opposing pores of the samples. Finally, tortuosity values obtained with each technique are used to assess the anisotropy of the samples. We applied the documented workflow to core- and pore-scale images. The CT scan images in the core-scale domain belong to a siliciclastic formation. Micro-CT scan images in the pore-scale domain were obtained from Berea Sandstone, Austin Chalk, and Estaillades limestone formations. We observed differences in estimates of direction-dependent electrical, diffusional, hydraulic, and geometrical tortuosity for both types of images. The highest numerical differences were observed when comparing streamline electrical and hydraulic tortuosity with diffusional tortuosity. The observed differences were significant in anisotropic samples. Differences in tortuosity estimates can impact the outcomes of rock physics models for which tortuosity is an input. The documented comparison provides insight in the selection of techniques for tortuosity estimation. Use of core-scale image data provides semicontinuous estimates of tortuosity and tortuosity anisotropy, which are typically not attainable using pore-scale images. Additionally, the semicontinuous tortuosity anisotropy estimates from whole-core CT scan images provide a tool for selection of best locations to take core plugs.

Optical fiber networks are being implemented in several countries aiming at dissemination of ultra-stable time and frequency references. This enables the comparison of optical clocks, which is a key part of the roadmap towards the future redefinition of the International System of Units (SI) second. Furthermore, this enables uses in chronometric geodesy, where the sensitivity of the optical clocks to the gravitation field enables measurements of height differences as low as 1 cm [1].The frequency signals in the optical fibers are sensitive to acoustic vibrations which are present in the ground, which is the main source of noise to the disseminated signals.In recent years, this has enabled studies in the use of optical fiber links for the detection of earthquakes [2]. In such an approach, the measurement is the integrated noise over the fiber path. This typically allows for one to several orders of magnitudes longer range as compare to DAS techniques, but with the loss of localization along the fiber. Such integrated approaches include measurements of the total polarization change of the light along the fiber [3], or the total phase change of a coherent ultra-stable laser signal, potentially including distributed sensing techniques in submarine fibers [2,4].Here, we will present the first quantitative studies on the use of coherent optical fiber links for seismic detection. Using a the fiber network REFIMEVE in France (see Fig. 1), we present studies on the sensitivity of coherent optical fiber links to seismic events. We describe the dependence of the sensitivity to a number of parameters like incident angle, magnitude and distance, and compare the sensitivity of a fiber link with that of conventional seismometers. We show, for a first time to our knowledge, the detection of seismic waves by a coherent optical fiber network, and we study the prospects of using such a network for the localization of earthquakes. Lastly, we discuss the principles and results of a machine learning algorithm, which enables automatic detection of earthquakes in a coherent optical fiber link.Bibliography:1. M. Takamoto et al., Test of general relativity by a pair of transportable optical lattice clocks, Nat. Phot., 14 (7), 411–415. doi:30210.1038/s41566-020-0619-82. G. Marra et al. , Ultrastable laser interferometry for earthquake detection with terrestrial and submarine cables. Science, eaat4458. doi: 10.1126/science.aat44582793. J.C. Castellanos et al. ,Optical polarization-based sensing and localization of submarine earthquakes. In Optical fiber communication conference (OFC) 2022, doi:26210.1364/OFC.2022.M1H.44. G. Marra et al., Optical interferometry–based array of seafloor environmental sensors using a transoceanic submarine cable. Science, doi: 10.1126/science.abo193Figure 1 : Map of the French REFIMEVE fiber network, shown in red lines. Dotted lines indicates indicate the full scale of the planned network, and continuous red lines indicate links used in these studies. Blue lines indicates the linear approximations of the links. All seismometers of the RESIF network is shown by small green triangles, and seismometers used in theses studies are shown by larger, turquoise triangles.

Estimation of porosity at a millimeter scale would be an order of magnitude finer resolution than traditional logging techniques. This enables proper description of reservoirs with thin layers and fine scale heterogeneities. To achieve this, we propose an end-to-end convolutional neural network (CNN) regression model that automatically predicts continuous porosity at a millimeter scale resolution using two-dimensional whole core CT scan images. More specifically, a CNN regression model is trained to learn from routine core analysis (RCA) porosity measurements. To characterize the performance of such approach, we compare the performance of this model with two linear regression models trained to learn the relationship between the average attenuation and standard deviation of the same two-dimensional images and RCA porosity. Our investigations reveal that the linear models are outperformed by the CNN, indicating the capability of the CNN model in extracting textures that are important for porosity estimations. We compare the predicted porosity results against the total porosity logs calculated from the density log. The obtained results show that the predicted porosity values using the proposed CNN method are well correlated with the core plug measurements and the porosity log. More importantly, the proposed approach can provide accurate millimeter scale porosity estimations, while the total porosity log is averaged over an interval and thus do not show such fine scale variations. Thus, the proposed method can be employed to calibrate the porosity logs, thereby reducing the uncertainties associated with indirect calculations of the porosity from such logs.

Summary Core measurements are used for rock classification and improved formation evaluation in both cored and noncored wells. However, the acquisition of such measurements is time-consuming, delaying rock classification efforts for weeks or months after core retrieval. On the other hand, well-log-based rock classification fails to account for rapid spatial variation of rock fabric encountered in heterogeneous and anisotropic formations due to the vertical resolution of conventional well logs. Interpretation of computed tomography (CT) scan data has been identified as an attractive and high-resolution alternative for enhancing rock texture detection, classification, and formation evaluation. Acquisition of CT scan data is accomplished shortly after core retrieval, providing high-resolution data for use in petrophysical workflows in relatively short periods of time. Typically, CT scan data are used as two-dimensional (2D) cross-sectional images, which is not suitable for quantification of three-dimensional (3D) rock fabric variation, which can increase the uncertainty in rock classification using image-based rock-fabric-related features. The methods documented in this paper aim to quantify rock-fabric-related features from whole-core 3D CT scan image stacks and slabbed whole-core photos using image analysis techniques. These quantitative features are integrated with conventional well logs and routine core analysis (RCA) data for fast and accurate detection of petrophysical rock classes. The detected rock classes are then used for improved formation evaluation. To achieve the objectives, we conducted a conventional formation evaluation. Then, we developed a workflow for preprocessing of whole-core 3D CT-scan image stacks and slabbed whole-core photos. Subsequently, we used image analysis techniques and tailor-made algorithms for the extraction of image-based rock-fabric-related features. Then, we used the image-based rock-fabric-related features for image-based rock classification. We used the detected rock classes for the development of class-based rock physics models to improve permeability estimates. Finally, we compared the detected image-based rock classes against other rock classification techniques and against image-based rock classes derived using 2D CT scan images. We applied the proposed workflow to a data set from a siliciclastic sequence with rapid spatial variations in rock fabric and pore structure. We compared the results against expert-derived lithofacies, conventional rock classification techniques, and rock classes derived using 2D CT scan images. The use of whole-core 3D CT scan image-stacks-based rock-fabric-related features accurately captured changes in the rock properties within the evaluated depth interval. Image-based rock classes derived by integration of whole-core 3D CT scan image-stacks-based and slabbed whole-core photos-based rock-fabric-related features agreed with expert-derived lithofacies. Furthermore, the use of the image-based rock classes in the formation evaluation of the evaluated depth intervals improved estimates of petrophysical properties such as permeability compared to conventional formation-based permeability estimates. A unique contribution of the proposed workflow compared to the previously documented rock classification methods is the derivation of quantitative features from whole-core 3D CT scan image stacks, which are conventionally used qualitatively. Furthermore, image-based rock-fabric-related features extracted from whole-core 3D CT scan image stacks can be used as a tool for quick assessment of recovered whole core for tasks such as locating best zones for extraction of core plugs for core analysis and flagging depth intervals showing abnormal well-log responses.

Influenza circulation declined during the COVID-19 pandemic. The timing and extent of decline and its association with interventions against COVID-19 were described for some regions. Here, we provide a global analysis of the influenza decline between March 2020 and September 2021 and investigate its potential drivers. We computed influenza change by country and trimester relative to the 2014-2019 period using the number of samples in the FluNet database. We used random forests to determine important predictors in a list of 20 covariates including demography, weather, pandemic preparedness, COVID-19 incidence, and COVID-19 pandemic response. With a regression tree we then classified observations according to these predictors. We found that influenza circulation decreased globally, with COVID-19 incidence and pandemic preparedness being the two most important predictors of this decrease. The regression tree showed interpretable groups of observations by country and trimester: Europe and North America clustered together in spring 2020, with limited influenza decline despite strong COVID-19 restrictions; in the period afterwards countries of temperate regions, with high pandemic preparedness, high COVID-19 incidence and stringent social restrictions grouped together having strong influenza decline. Conversely, countries in the tropics, with altogether low pandemic preparedness, low reported COVID-19 incidence and low strength of COVID-19 response showed low influenza decline overall. A final group singled out four “zero-Covid” countries, with the lowest residual influenza levels. The spatiotemporal decline of influenza during the COVID-19 pandemic was global, yet heterogeneous. The sociodemographic context and stage of the COVID-19 pandemic showed non-linear associations with this decline. Zero-Covid countries maintained the lowest levels of reduction with strict border controls and despite close-to-normal social activity. These results suggest that the resurgence of influenza could take equally diverse paths. It also emphasises the importance of influenza reseeding in driving countries’ seasonal influenza epidemics.

Carbon capture and storage (CCS) is an attractive alternative to reduce the concentration of greenhouse gases in the atmosphere with the objective of preventing further increases in global temperature. Accurate estimation of Petrophysical properties and detection of rock types are critical for the assessment of key aspects of CCS projects in geological formations such as storage capacity, injectivity, trapping mechanisms, and containment. The objectives of this paper are (a) to use whole-core computed tomography (CT) scan images and core photos, conventional well logs, nuclear magnetic resonance (NMR) logs, and core-measured properties for automated rock classification, (b) to develop class-based rock physics models for enhanced petrophysical properties estimation, and (c) to provide a method to expedite the detection of quantitative image-based rock classes. First, we conducted conventional formation evaluation for initial assessment of petrophysical properties. Then, we employed image analysis techniques to remove non-core material visual elements from the available image data (i.e., CT-scan images and core photos). Afterwards, we extracted rock-fabric related features from the available image data. We characterized the pore structure of the evaluated interval using NMR logs. We integrated conventional well logs and routine core analysis (RCA) data with image-based features and NMR pore structure parameters to automatically detected rock classes by means of a physics-based cost function. Finally, we updated the estimated petrophysical properties employing class-based rock physics models and compared the obtained result against conventional formation evaluation estimates. We applied the proposed workflow to the pilot well drilled in a saline water aquifer formation that will be used for CO2 injection and storage in the Northern Lights CCS project. The extracted image-based rock fabric features were in agreement with the visual aspect of the evaluated depth intervals. The detected rock classes captured the fluid-flow behavior using a permeability-based cost function, the variation in petrophysical and compositional properties trough well logs, and quantitative rock fabric of the evaluated depth interval through the core image data. Finally, the use of class-based rock physics models improved permeability estimates decreasing the mean relative error by 27% compared to formation-based permeability estimates from a conventional method (formation-based porosity-permeability correlations). One of the key contributions of the proposed workflow is that it integrates conventional well logs, core-measured properties, NMR logs, and high-resolution image data. As a result, the obtained integrated rock classes capture key petrophysical and geological parameters of the evaluated depth intervals that are typically not included in rock classification efforts. The obtained integrated rock classes can potentially improve the development of accurate geological models, which are employed in simulation efforts as a screening tool for selection of geological formations for CO2 storage as well as for storage capacity, selection of CO2 injection intervals, and containment forecasting.

Summary X-ray computerized tomography (CT) is a nondestructive method of providing information about the internal composition and structure of whole core reservoir samples. In this study we propose a method to classify lithology. The novelty of this method is that it uses statistical and textural information extracted from whole core CT images in a supervised learning environment. In the proposed approaches, first-order statistical features and textural grey-level co-occurrence matrix (GLCM) features are extracted from whole core CT images. Here, two workflows are considered. In the first workflow, the extracted features are used to train a support vector machine (SVM) to classify lithofacies. In the second workflow, a principal component analysis (PCA) step is added before training with two purposes: first, to eliminate collinearity among the features and second, to investigate the amount of information needed to differentiate the analyzed images. Before extracting the statistical features, the images are preprocessed and decomposed using Haar mother wavelet decomposition schemes to enhance the texture and to acquire a set of detail images that are then used to compute the statistical features. The training data set includes lithological information obtained from core description. The approach is validated using the trained SVM and hybrid (PCA + SVM) classifiers to predict lithofacies in a set of unseen data. The obtained results show that the SVM classifier can predict some of the lithofacies with high accuracy (up to 91% recall), but it misclassifies, to some extent, similar lithofacies with similar grain size, texture, and transport properties. The SVM classifier captures the heterogeneity in the whole core CT images more accurately compared with the core description, indicating that the CT images provide additional high-resolution information not observed by manual core description. Further, the obtained prediction results add information on the similarity of the lithofacies classes. The prediction results using the hybrid classifier are worse than the SVM classifier, indicating that low-power components may contain information that is required to differentiate among various lithofacies.

Introduction The aim of this study was to evaluate the impact of the contouring methods on dose metrics and their predictive value on tumor control and survival, in both situations of pre-treatment and post-treatment dosimetry, for patients with advanced HCC treated with SIRT. Methods Forty-eight patients who underwent SIRT between 2012 and 2020 were retrospectively included in this study. Target volumes were delineated using two methods: MRI-based contours manually drawn by a radiologist and then registered on SPECT/CT and PET/CT via deformable registration (Pre-CMRI and Post-CMRI), 99mTc-MAA-SPECT and 90Y-microspheres-PET 10% threshold contouring (Pre-CSPECT and Post-CPET). The mean absorbed dose (Dm) and the minimal absorbed dose delivered to 70% of the tumor volume (D70) were evaluated with both contouring methods; the tumor-to-normal liver uptake ratio (TNR) was evaluated with MRI-based contours only. Tumor response was assessed using the mRECIST criteria on the follow-up MRIs. Results No significant differences were found for Dm and TNR between pre- and post-treatment. TNR evaluated with radiologic contours (Pre-CMRI and Post-CMRI) were predictive of tumor control at 6 months on pre- and post-treatment dosimetry (OR 5.9 and 7.1, respectively; p = 0.02 and 0.01). All dose metrics determined with both methods were predictive of overall survival (OS) on pre-treatment dosimetry, but only Dm with MRI-based contours was predictive of OS on post-treatment images with a median of 23 months for patients with a supramedian Dm versus 14 months for the others (p = 0.04). Conclusion In advanced HCC treated with SIRT, Dm and TNR determined with radiologic contours were predictive of tumor control and OS. This study shows that a rigorous clinical workflow (radiologic contours + registration on scintigraphic images) is feasible and should be prospectively considered for improving therapeutic strategy.

This papers derives finite sample results to assess the consistency of Generalized Pareto regression trees introduced by Farkas et al. [2021] as tools to perform extreme value regression for heavy-tailed distributions. This procedure allows one to constitute clusters of similar tail behaviors depending on the value of the covariates, based on a recursive partition of the sample and simple model selection rules. The results we provide are obtained from concentration inequalities, and are valid for a finite sample size. A misspecification bias that arises from the use of a Peaks over Threshold approach is also taken into account. Moreover, the derived properties legitimate the pruning strategies, that is the model selection rules, used to select a proper tree that achieves a compromise between simplicity and good fit. The methodology is illustrated through a simulation study, and a real data application in insurance against natural disasters.

In this work, a new dual modality monitoring technique is presented to demonstrate its interest to investigate the salt precipitation dynamics induced by gas flow-through drying. It consists of imaging simultaneously a core flood using both Neutron and X-ray beams. A method to calibrate and process the two signals is presented. It takes advantage of the difference in attenuation between the two ionizing radiations to quantify the different phase saturations and compositions as well as the reduction of porosity caused by salt precipitation. A set of experiments has been conducted at the NeXT-Grenoble beamline of the Institute Laue-Langevin facilities (ILL, France). Experiments were conducted on a homogeneous rock sample of Bentheimer sandstone using dry nitrogen and a 100 g/L KBr brine. The two first experiments aimed to calibrate the dual modality for the different phases. The last two experiments have been conducted with a brine capillary contact maintained at the gas outlet. Experimental data have given new insights into the organization of the three phases (the brine, the gas, and the precipitated salt) when a salt bank is formed in the sample. These quantities computed using dual-modality imaging show great similarities with published work. The salt accumulation was used to estimate the flow rate of brine pumped through the capillary contact to compensate for the brine evaporation in the gas phase. Observations have shown that a reduction of the initial porosity in some sections of the sample by 12–14% was enough to trigger a gas draw-down characterized by the migration of the salt toward the gas inlet. In some conditions (low gas inlet pressure for example), the rise of the water could be fast enough to form a second salt bank higher in the sample. It has been observed that the formation of the second salt bank could spread the precipitated salt in a less damaging configuration for the gas flow, triggering a phase of gas build-up characterized by the withdrawal of the water. These phases of gas draw-down and build-up could alternate until the sample clogs.

The study’s purpose was to assess the safety, feasibility and efficiency of selective arterial embolization (SAE) using N-butyl cyanoacrylate (NBCA) glue before percutaneous cryoablation (PCA) of renal malignancies in patients whose tumor characteristics and/or comorbidities resulted in an unacceptable risk of bleeding. In this single-center retrospective study of 19 consecutive high-risk patients (median age, 74 years) with renal malignancies managed in 2017–2020 by SAE with NBCA followed by PCA, data about patients, tumor and procedures characteristics, complications, renal function and hemoglobin concentration before and after treatment, as well as recurrence were collected. Charlson comorbidity index was ≥4 in 89.5% of patients. Ten patients were treated by antiplatelet and/or anticoagulant therapy. Median tumor largest diameter was 3.75 cm (range, 1–6.5 cm) and R.E.N.A.L. nephrometry score was ≥7 in 80%, indicating substantial tumor complexity. No major complications were recorded and minor complications occurred in 7 patients. No residual tumor was found at 6-week imaging follow-up in 18/19 patients. Tumor recurrence was visible in 1/16 patients at 6-month imaging follow-up. No significant difference was found for renal function after treatment (p = 0.07), whereas significant decrease in hemoglobin concentration was noted (p = 0.00004), although it was relevant for only one patient who required only blood transfusion and no further intervention. SAE prior to PCA is safe and effective for managing renal malignancies in high-risk patients.

Depositional mechanisms of sediments and post-depositional process often cause spatial variation and heterogeneity in rock fabric, which can impact the directional dependency of petrophysical, electrical, and mechanical properties. Quantification of the directional dependency of the aforementioned properties is fundamental for the appropriate characterization of hydrocarbon-bearing reservoirs. Anisotropy quantification can be accomplished through numerical simulations of physical phenomena such as fluid flow, gas diffusion, and electric current conduction in porous media using multi-scale image data. Typically, the outcome of these simulations is a transport property (e.g., permeability). However, it is also possible to quantify the tortuosity of the media used as simulation domain, which is a fundamental descriptor of the microstructure of the rock. The objectives of this paper are (a) to quantify tortuosity anisotropy of porous media using multi-scale image data (i.e., whole-core CT-scan and micro-CT-scan image stacks) through simulation of electrical potential distribution, diffusion, and fluid flow, and (b) to compare electrical, diffusional, and hydraulic tortuosity. First, we pre-process the images (i.e., CT-scan images) to remove non-rock material visual elements (e.g., core barrel). Then, we perform image analysis to identify different phases in the raw images. Then, we proceed with the numerical simulations of electric potential distribution. The simulation results are utilized as inputs for a streamline algorithm and subsequent direction-dependent electrical tortuosity estimation. Next, we conduct numerical simulation of diffusion using a random walk algorithm. The distance covered by each walker in each cartesian direction is used to compute the direction-dependent diffusional tortuosity. Finally, we conduct fluid-flow simulations to obtain the velocity distribution and compute the direction-dependent hydraulic tortuosity. The simulations are conducted in the most continuous phase of the segmented whole-core CT-scan image stacks and in the segmented pore-space of the micro-CT-scan image stacks. Finally, the direction-dependent tortuosity values obtained with each technique are employed to assess the anisotropy of the evaluated samples. We tested the introduced workflow on dual energy whole-core CT-scan images and on smaller scale micro-CT-scan images. The whole-core CT-scan images were obtained from a siliciclastic depth interval, composed mainly by spiculites. Micro-CT-scan images we obtained from Berea Sandstone and Austin Chalk formations. We observed numerical differences in the estimates of direction-dependent electrical, diffusional, and hydraulic tortuosity for both types of image data employed. The highest numerical differences were observed when comparing electrical and hydraulic tortuosity with diffusional tortuosity. The observed differences were significant specially in anisotropic samples. The documented comparison provides useful insight in the selection process of techniques for estimation of tortuosity. The use of core-scale image data in the proposed workflow provides semi-continuous estimates of tortuosity and tortuosity anisotropy which is typically not attainable when using pore-scale images. Additionally, the semi-continuous nature of the tortuosity and tortuosity anisotropy estimates in whole-core CT-scan image data provides an excellent tool for the selection of core plugs coring locations.

La revolution numerique a pour corollaire l’apparition de nouveaux risques. Face a la montee de la menace cyber, les contrats d’assurance representent un outil fondamental pour la resilience de nos societes. Si le marche de l’assurance cyber est en rapide expansion, la quantification des risques economiques associes est encore balbutiante. Elle necessite en effet la conception de modeles predictifs sophistiques – afin de traduire la complexite des phenomenes et leur grande volatilite, notamment le risque d’accumulation de sinistres – tout en tenant compte de la faiblesse des informations utilisees pour les calibrer. Dans cet article, nous mettons en evidence les difficultes rencontrees pour la collecte de donnees pertinentes, et l’impact des comportements des agents sur l’evaluation du risque. Nous exposons egalement un certain nombre de pistes methodologiques explorees pour surmonter ces difficultes. Classification JEL : G11, G22, O33.

Conventional formation evaluation provides fast and accurate estimations of petrophysical properties in conventional formations through conventional well logs and routine core analysis (RCA) data. However, as the complexity of the evaluated formations increases conventional formation evaluation fails to provide accurate estimates of petrophysical properties. This inaccuracy is mainly caused by rapid variation in rock fabric (i.e., spatial distribution of rock components) not properly captured by conventional well logging tools and interpretation methods. Acquisition of high-resolution whole-core computed tomography (CT) scanning images can help to identify rock-fabric-related parameters that can enhance formation evaluation. In a recent publication, we introduced a permeability-based cost function for rock classification, optimization of the number of rock classes, and estimation of permeability. Incorporation of additional petrophysical properties into the proposed cost function can improved the reliability of the detected rock classes and ultimately improve the estimation of class-based petrophysical properties. The objectives of this paper are (a) to introduce a robust optimization method for rock classification and estimation of petrophysical properties, (b), to automatically employ whole-core two-dimensional (2D) CT-scan images and slabbed whole-core photos for enhanced estimates of petrophysical properties, (c) to integrate whole-core CT-scan images and slabbed whole-core photos with well logs and RCA data for automatic rock classification, (d) to derive class-based rock physics models for improved estimates of petrophysical properties. First, we conducted formation evaluation using well logs and RCA data for estimation of petrophysical properties. Then, we derived quantitative features from 2D CT-scan images and slabbed whole-core photos. We employed image-based features, RCA data and CT-scan-based bulk density for optimization of the number rock classes. Optimization of rock classes was accomplished using a physics-based cost function (i.e., a function of petrophysical properties of the rock) that compares class-based estimates of petrophysical properties (e.g., permeability and porosity) with core-measured properties for increasing number of image-based rock classes. The cost function is computed until convergence is achieved. Finally, we used class-based rock physics models for improved estimates of porosity and permeability. We demonstrated the reliability of the proposed method using whole-core CT-scan images and core photos from two siliciclastic depth intervals with measurable variation in rock fabric. We used well logs, RCA data, and CT-scan-based bulk-density. The advantages of using whole-core CT-scan data are two-fold. First, it provides high-resolution quantitative features that capture rapid spatial variation in rock fabric allowing accurate rock classification. Second, the use of CT-scan-based bulk density improved the accuracy of class-based porosity-bulk density models. The optimum number of rock classes was consistent for all the evaluated cost functions. Class-based rock physics models improved the estimates of porosity and permeability values. A unique contribution of the introduced workflow when compared to previously documented image-based rock classification workflows is that it simultaneously improves estimates of both porosity and permeability, and it can capture rock class that might not be identifiable using conventional rock classification techniques.

CT scan images provide valuable three-dimensional information on the mineralogical composition and overall internal structure of cores. X-ray computerized tomography (CT) imaging of whole cores has therefore become a routine step in core analysis workflows. This new data type gives new possibilities in reservoir characterization. Lithological classification of reservoir rocks, in its turn, is an essential step to better understand the depositional environment and for subsequent effective reservoir characterization: the chemical composition of the minerals, combined with their grain size, sorting and pore size distribution is known to highly affect the transport properties of reservoir rocks. Lithological classification on the extracted whole core material is thus consequential; however, it also requires significant investments, being traditionally conducted through visual inspection performed by expert geologists. This manual process is time consuming, and prone to subjective interpretations and human errors. Therefore, a current research and development trend is to find automated methods for computer-assisting the assessment of this type of data, eventually reducing time and costs of core analysis, and improving the overall business decisions. In this study we explore the application of Convolutional Neural Networks (CNN) to automatically classify lithofacies. We propose a workflow for high resolution lithofacies classification using whole core three-dimensional CT images, and we assess the validity of our approach on a field-example from the Norwegian continental shelf. The novelty of our approach is thus learning, through a CNN, the relationship between convolution-derived three-dimensional features and expert-derived lithofacies classes. We thus extend approaches working on two-dimensional images into a workflow that uses high-resolution three-dimensional CT images as direct input. In our work the training data set includes information obtained from manual core description. Prior to training, the three-dimensional CT images are pre-processed so that undesired artefacts are automatically flagged and removed before being fed into the network. The approach is validated using the trained CNN classifier to predict lithofacies in a set of unseen three-dimensional CT data. The trained model can predict lithofacies classes with high accuracy, with a misclassification rate of about 3%. We found that these misclassifications are mainly associated with the presence of high-density material such as pyrite nodules and drilling mud invasions. Dipping fractures and missing values, not completely removed by image pre-processing, are additional reasons for model deficiency in some of the incorrectly classified images. Overall, the trained classifier exhibits higher pixel-wise precision and captures the high-resolution heterogeneities more accurately compared to the manual core descriptions.

We introduce multi-branch repeater laser stations (MLSs) for the dissemination of an ultra-stable signal from one point to multiple users and the simultaneous evaluation of the stability and accuracy of multiple links. We perform the study of the noise floor of this new instrument. We present then an optical fiber network of 4800 km built with three MLSs and 13 repeater laser stations (RLSs). We show the multi-user optical frequency dissemination on four links totalizing 2198 km with uncertainties below 1.1 × 10−19. The robustness of the network over two years is presented and stability and accuracy at 107 seconds integration time are finally showed.

In this paper, we propose a general framework to design accumulation scenarios that can be used to anticipate the impact of a massive cyber attack on an insurance portfolio. The aim is also to emphasize the role of countermeasures in stopping the spread of the attack over the portfolio, and to quantify the benefits of implementing such strategies of response. Our approach consists of separating the global dynamic of the cyber event (that can be described through compartmental epidemiological models), the effect on the portfolio, and the response strategy. This general framework allows us to obtain Gaussian approximations for the corresponding processes, and sharp confidence bounds for the losses. A detailed simulation study, which mimics the effects of a Wannacry scenario, illustrates the practical implementation of the method.

International audience; We aim to demonstrate the first mise en pratique of the kelvin, within the framework of the new International System of Units (SI), with a direct link to the atomic fountains of LNE-SYRTE laboratory, the French time-frequency metrology institute. The Boltzmann constant being fixed in the new SI, the mise en pratique of the kelvin can be cast as a frequency measurement. One method considered, Doppler Broadening thermometry (DBT), originally proposed and initially demonstrated at Laboratoire de Physique des Lasers is being developed by us and others. We report our recent efforts towards implementing DBT with a direct link to the primary frequency standard in the 300-430K range to demonstrate its potential.

International audience; In the insurance broker market, commissions received by brokers are closely related to so-called "customer value": the longer a policyholder keeps their contract, the more profit there is for the company and therefore the broker. Hence, predicting the time at which a potential policyholder will surrender their contract is essential in order to optimize a commercial process and define a prospect scoring. In this paper, we propose a weighted random forest model to address this problem. Our model is designed to compensate for the impact of random censoring. We investigate different types of assumptions on the censor-ing, studying both the cases where it is independent or not from the covariates. We compare our approach with other standard methods which apply in our setting, using simulated and real data analysis. We show that our approach is very competitive in terms of quadratic error in addressing the given problem.

In this paper, we develop a methodology to automatically classify claims using the information contained in text reports (redacted at their opening). From this automatic analysis, the aim is to predict if a claim is expected to be particularly severe or not. The difficulty is the rarity of such extreme claims in the database, and hence the difficulty, for classical prediction techniques like logistic regression to accurately predict the outcome. Since data is unbalanced (too few observations are associated with a positive label), we propose different rebalance algorithm to deal with this issue. We discuss the use of different embedding methodologies used to process text data, and the role of the architectures of the networks.

Thanks to nonparametric estimators coming from machine learning, microlevel reserving has become more and more popular for actuaries. Recent research focused on how to integrate the whole information one can have on claims to predict individual reserves, with varying success due to incomplete observations. Using the CART algorithm, we develop new results that allow us to deal with large reporting delays and partially observed explanatory variables. Statistically speaking, we extend CART to take into account truncation of the data, and introduce plug-in estimators. Our applications are based on real-life insurance portfolios embedding Income Protection and Third-Party Liability guarantees. The full knowledge of the claim lifetime is shown to be crucial to predict the individual reserves efficiently.

Countries with common features in terms of social, economic and health systems generally have mortality trends which evolve in a similar manner. Drawing on this, many multi-population models are built on a coherence assumption which inhibits the divergence of mortality rates between two populations, or more, on the long run. However, this assumption may prove to be too strong in a general context, especially when it is imposed to a large collection of countries. We also note that the coherence hypothesis significantly reduces the spectrum of achievable mortality dispersion forecasts for a collection of populations when comparing to the historical observations. This may distort the longevity risk assessment of an insurer. In this paper, we propose a new model to forecast multiple populations assuming that the long-run coherent principle is verified by subgroups of countries that we call the "locally coherence" property. Thus, our specification is built on a trade-off between the Lee-Carter's diversification and Li-Lee's concentration features and allows to fit the model to a large number of populations simultaneously. A penalized vector autoregressive (VAR) model, based on the elastic-net regularization, is considered for modeling the dynamics of common trends between subgroups. Furthermore, we apply our methodology on 32 European populations mortality data and discuss the behavior of our model in terms of simulated mortality dispersion. Within the Solvency II directive, we quantify the impact on the longevity risk solvency capital requirement of an insurer for a simplified pension product. Finally, we extend our model by allowing populations to switch from one coherence group to another. We then analyze its incidence on longevity hedges basis risk assessment. JEL Classification: C18, C32, C53, J11.

International audience; We introduce so-called single-index copulas. They are semi-parametric conditional copulas whose parameter is an unknown link function of a univariate index only. We propose estimates of this link function and of the finite-dimensional unknown parameter. The asymptotic properties of the latter estimates are stated. Thanks to some properties of conditional Kendall's tau, we illustrate our technical conditions with several usual copula families.

We present an experimental investigation of the stability limits specific to optical frequency standards using fiber optic architecture and semiconductor lasers. A compact setup composed of a semiconductor laser frequency-locked onto an acetylene transition detected in saturated absorption has been implemented using only fiber optic components. Fiber optic technology allows compact and reliable solutions for various applications. However, for high sensitivity and stability applications such as metrology, residual reflections induced by optical index inhomogeneities in connectors and fiber-coupled components leading to spurious interference significantly limit performance. We have examined the origin of the interference fringes superimposed on the detected signal and the limitations they cause to the frequency stability of the reference. The effects of temperature and beam power fluctuations are also examined. Our results show that the frequency stability is limited in the 10 − 13 range by the effect of interference fringes due to use of fiber components.

This article provides an overview of the current state of digital rock technology, with emphasis on industrial applications. We show how imaging and image analysis can be applied for rock typing and modeling of end-point saturations. Different methods to obtain a digital model of the pore space from pore scale images are presented, and the strengths and weaknesses of the different methods are discussed. We also show how imaging bridges the different subjects of geology, petrophysics and reservoir simulations, by being a common denominator for results in all these subjects. Network modeling is compared to direct simulations on grid models, and their respective strengths are discussed. Finally we present an example of digital rock technology applied to a sandstone oil reservoir. Results from digital rock modeling are compared to results from traditional laboratory experiments. We highlight the mutual benefits from conducting both traditional experiments and digital rock modeling., Comment: 36 pages, 18 figures

High-quality frequency transfer based on existing telecommunication fiber links allows state-of-the-art microwave and optical clocks to be compared on a continental and potentially even intercontinental scale. We present a half-a-year-long data set of unidirectional optical frequency transfer over a 2 × 43 k m urban fiber link. We observe a relative frequency instability of 8.0 × 10 − 16 at 1 s and at best 6.0 × 10 − 18 at 10 6 s integration time. Our results show that the unidirectional two-way method gives the possibility to perform comparisons of international primary standards over fiber links faster than over satellite links. Moreover, we investigate the major limiting factors of such a unidirectional setup.

We present a study of the fundamental limit of fiber links using dedicated link architecture. We use an experimental arrangement that enables us to detect the forward and backward propagation noise independently and simultaneously in optical fiber and where the optical phase evolution is expected to be driven by the only contribution of the reference arms of the Michelson interferometer ensemble. In this article, we demonstrate indeed the high correlation between the optical phase evolution and the temperature variation of the interferometer ensemble, leading to a frequency offset of (4.4±2.3)×10