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Renard, Philippe
Nom
Renard, Philippe
Affiliation principale
Fonction
Directeur de Recherche
Email
Philippe.Renard@unine.ch
Identifiants
RĂ©sultat de la recherche
Voici les éléments 1 - 10 sur 11
- PublicationAccès libreA parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations(2022)
; ; Multiple-point statistics algorithms allow modeling spatial variability from training images. Among these techniques, the Direct Sampling (DS) algorithm has advanced capabilities, such as multivariate simulations, treatment of non-stationarity, multi-resolution capabilities, conditioning by inequality or connectivity data. However, finding the right trade-off between computing time and simulation quality requires tuning three main parameters, which can be complicated since simulation time and quality are affected by these parameters in a complex manner. To facilitate the parameter selection, we propose the Direct Sampling Best Candidate (DSBC) parametrization approach. It consists in setting the distance threshold to 0. The two other parameters are kept (the number of neighbors and the scan fraction) as well as all the advantages of DS. We present three test cases that prove that the DSBC approach allows to identify efficiently parameters leading to comparable or better quality and computational time than the standard DS parametrization. We conclude that the DSBC approach could be used as a default mode when using DS, and that the standard parametrization should only be used when the DSBC approach is not sufficient. - PublicationAccès libreDirect simulation of non-additive properties on unstructured grids(2020-6)
; ; - PublicationAccès libreOil production uncertainty assessment by predicting reservoir production curves and confidence intervals from arbitrary proxy responses(2019)
- PublicationAccès libreRandom partitioning and adaptive filters for multiple-point stochastic simulation(2018)
; Amirfattahi, RMultiple-point geostatistical simulation is used to simulate the spatial structures of geological phenomena. In contrast to conventional two-point variogram based geostatistical methods, the multiple-point approach is capable of simulating complex spatial patterns, shapes, and structures normally observed in geological media. A commonly used pattern based multiple-point geostatistical simulation algorithms is called FILTERSIM. In the conventional FILTERSIM algorithm, the patterns identified in training images are transformed into filter score space using fixed filters that are neither dependent on the training images nor on the characteristics of the patterns extracted from them. In this paper, we introduce two new methods, one for geostatistical simulation and another for conditioning the results. At first, new filters are designed using principal component analysis in such a way to include most structural information specific to the governing training images resulting in the selection of closer patterns in the filter score space. We then propose to combine adaptive filters with an overlap strategy along a raster path and an efficient conditioning method to develop an algorithm for reservoir simulation with high accuracy and continuity. We also combine image quilting with this algorithm to improve connectivity a lot. The proposed method, which we call random partitioning with adaptive filters simulation method, can be used both for continuous and discrete variables. The results of the proposed method show a significant improvement in recovering the expected shapes and structural continuity in the final simulated realizations as compared to those of conventional FILTERSIM algorithm and the algorithm is more than ten times faster than FILTERSIM because of using raster path and using small overlap specially when we use image quilting. - PublicationAccès libreGeothermal state of the deep Western Alpine Molasse Basin, France-Switzerland(2017)
; Rusillon, EOver the last few years the Western Alpine Molasse Basin (WAMB) has been attracting large institutional, industrial and scientific interest to evaluate the feasibility of geothermal energy production. However, the thermal state of the basin, which is instrumental to the development of such geothermal projects, has remained to date poorly known. Here, we compile and correct temperature measurements (mostly bottom hole temperature) from 26 existing well data mostly acquired during former hydrocarbon exploration in the basin. These data suggest that the average geothermal gradient of the WAMB is around 25–30 °C/km. We further use these data to build the first well data-driven 3D geostatistical temperature model of the whole basin and generate probabilistic maps of isotherms at 70 and 140 °C. This model highlights a number of positive and negative thermal anomalies that are interpreted in the context of heat advection caused by fluid circulation along faults and/or karst systems. This study confirms that the WAMB has a great potential for low-enthalpy geothermal resources and presents a typology of advection-dominated potential targets. - PublicationAccès libreConstraining distance-based multipoint simulations to proportions and trends(2015-10)
; ; ; - PublicationAccès libreGeological realism in hydrogeological and geophysical inverse modeling: A review(2015)
; - PublicationAccès libre3D multiple-point statistics simulation using 2D training images(2012-3)
; One of the main issues in the application of multiple-point statistics (MPS) to the simulation of three-dimensional (3D) blocks is the lack of a suitable 3D training image. In this work, we compare three methods of overcoming this issue using information coming from bidimensional (20) training images. One approach is based on the aggregation of probabilities. The other approaches are novel. One relies on merging the lists obtained using the impala algorithm from diverse 2D training images, creating a list of compatible data events that is then used for the MPS simulation. The other (s2Dcd) is based on sequential simulations of 2D slices constrained by the conditioning data computed at the previous simulation steps. These three methods are tested on the reproduction of two 3D images that are used as references, and on a real case study where two training images of sedimentary structures are considered. The tests show that it is possible to obtain 3D MPS simulations with at least two 2D training images. The simulations obtained, in particular those obtained with the s2Dcd method, are close to the references, according to a number of comparison criteria. The CPU time required to simulate with the method s2Dcd is from two to four orders of magnitude smaller than the one required by a MPS simulation performed using a 3D training image, while the results obtained are comparable. This computational efficiency and the possibility of using MPS for 3D simulation without the need for a 3D training image facilitates the inclusion of MPS in Monte Carlo, uncertainty evaluation, and stochastic inverse problems frameworks. - PublicationAccès libreComparison of three geostatistical methods for hydrofacies simulation: a test on alluvial sediments(2012-1-10)
- PublicationAccès libreExtrapolating the Fractal Characteristics of an Image Using Scale-Invariant Multiple-Point Statistics(2011-10)
; ; The resolution of measurement devices can be insufficient for certain purposes. We propose to stochastically simulate spatial features at scales smaller than the measurement resolution. This is accomplished using multiple-point geostatistical simulation (direct sampling in the present case) to interpolate values at the target scale. These structures are inferred using hypothesis of scale invariance and stationarity on the spatial patterns found at the coarse scale. The proposed multiple-point super-resolution mapping method is able to deal with "both continuous and categorical variables", and can be extended to multivariate problems. The advantages and limitations of the approach are illustrated with examples from satellite imaging.