Estimation des propriétés hydrauliques d'un aquifère par simulation séquentielle gaussienne bayésienne

Estimation directe -- Inversion conjointe et inversion contrainte -- Estimation par intégration -- Notion de base d'hydrogéologie -- Relation entre les propriétés hydrauliques et électriques du sol -- Géophysique en forage -- Outils statistiques -- Méthodologie -- Approche proposée -- Déroulement de la simulation -- Validation de la méthode -- Résultats -- modèle synthétique -- Données et paramètres de pré-simulation -- Simulations -- Comparaison des simulations -- Comparaison des variances -- Comparaison des coefficients de corrélation -- Résultats -- site de St-Lambert -de-Lauzon -- Description du site à l'étude -- Données et paramètres de pré-simulation

Three-Dimensional Stochastic Estimation of Porosity Distribution: Benefits of Using Ground-Penetrating Radar Velocity Tomograms in Simulated-Annealing-Based or Bayesian Sequential Simulation Approaches

Estimation of the three-dimensional (3-D) distribution of hydrologic properties and related uncertainty is a key for improved predictions of hydrologic processes in the subsurface. However it is difficult to gain high-quality and high-density hydrologic information from the subsurface. In this regard a promising strategy is to use high-resolution geophysical data (that are relatively sensitive to variations of a hydrologic parameter of interest) to supplement direct hydrologic information from measurements in wells (e.g., logs, vertical profiles) and then generate stochastic simulations of the distribution of the hydrologic property conditioned on the hydrologic and geophysical data. In this study we develop and apply this strategy for a 3-D field experiment in the heterogeneous aquifer at the Boise Hydrogeophysical Research Site and we evaluate how much benefit the geophysical data provide. We run high-resolution 3-D conditional simulations of porosity with both simulated-annealing-based and Bayesian sequential approaches using information from multiple intersecting crosshole gound-penetrating radar (GPR) velocity tomograms and neutron porosity logs. The benefit of using GPR data is assessed by investigating their ability, when included in conditional simulation, to predict porosity log data withheld from the simulation. Results show that the use of crosshole GPR data can significantly improve the estimation of porosity spatial distribution and reduce associated uncertainty compared to using only well log measurements for the estimation. The amount of benefit depends primarily on the strength of the petrophysical relation between the GPR and porosity data, the variability of this relation throughout the investigated site, and lateral structural continuity at the site

Revealing inflow and wake conditions of a 6 MW floating turbine

We investigate the characteristics of the inflow and the wake of a 6 MW floating wind turbine from the Hywind Scotland offshore wind farm, the world's first floating wind farm. We use two commercial nacelle-mounted lidars to measure the up- and downwind conditions with a fixed and a scanning measuring geometry, respectively. In the analysis, the effect of the pitch and roll angles of the nacelle on the lidar measuring location is taken into account. The upwind conditions are parameterized in terms of the mean horizontal wind vector at hub height, the shear and veer of the wind profile along the upper part of the rotor, and the induction of the wind turbine rotor. The wake characteristics are studied in two narrow wind speed intervals between 8.5–9.5 and 12.5–13.5 m s−1, corresponding to below and above rotor rated speeds, respectively, and for turbulence intensity values between 3.3 %–6.4 %. The wake flow is measured along a horizontal plane by a wind lidar scanning in a plan position indicator mode, which reaches 10 D downwind. This study focuses on the downstream area between 3 and 8 D. In this region, our observations show that the transverse profile of the wake can be adequately described by a self-similar wind speed deficit that follows a Gaussian distribution. We find that even small variations (∼1 %–2 %) in the ambient turbulence intensity can result in an up to 10 % faster wake recovery. Furthermore, we do not observe any additional spread of the wake due to the motion of the floating wind turbine examined in this study.</p

Revealing inflow and wake conditions of a 6 MW floating turbine

We investigate the characteristics of the inflow and the wake of a 6 MW floating wind turbine from the Hywind Scotland offshore wind farm, the world's first floating wind farm. We use two commercial nacelle-mounted lidars to measure the up- and downwind conditions with a fixed and a scanning measuring geometry, respectively. In the analysis, the effect of the pitch and roll angles of the nacelle on the lidar measuring location is taken into account. The upwind conditions are parameterized in terms of the mean horizontal wind vector at hub height, the shear and veer of the wind profile along the upper part of the rotor, and the induction of the wind turbine rotor. The wake characteristics are studied in two narrow wind speed intervals between 8.5-9.5 and 12.5-13.5 m s-1, corresponding to below and above rotor rated speeds, respectively, and for turbulence intensity values between 3.3 %-6.4 %. The wake flow is measured along a horizontal plane by a wind lidar scanning in a plan position indicator mode, which reaches 10 D downwind. This study focuses on the downstream area between 3 and 8 D. In this region, our observations show that the transverse profile of the wake can be adequately described by a self-similar wind speed deficit that follows a Gaussian distribution. We find that even small variations (∼1 %-2 %) in the ambient turbulence intensity can result in an up to 10 % faster wake recovery. Furthermore, we do not observe any additional spread of the wake due to the motion of the floating wind turbine examined in this study

Heterogeneous aquifer characterization from ground-penetrating radar tomography and borehole hydrogeophysical data using nonlinear Bayesian simulations

It is known that the heterogeneity of hydraulic conductivity drives the groundwater flow and the transport of contaminants. However, in conventional characterization methods, the lack of densely sampled hydrological data does not permit us to describe the aquifer heterogeneity at an appropriate scale. In this study, we integrate ground-penetrating radar (GPR) tomographic data with hydraulic conductivity logs to estimate the hydraulic conductivity of a heterogeneous unconsolidated aquifer at a decimetric scale between two boreholes. The integration of these different data sets is achieved using a nonlinear Bayesian simulation algorithm. The prior hydraulic conductivity distribution is estimated, under Gaussian hypothesis, by simple kriging of the hydraulic well data. The likelihood of hydraulic conductivity given the relative permittivity and the electrical conductivity functions is obtained from a kernel probability density function estimator that describes the in-situ relationship between the electric and the hydraulic properties measured along boreholes. The proposed method is tested on a synthetic heterogeneous model of permeability to validate the methodology. We show that permeability realizations obtained from the proposed algorithm present a higher correlation with the synthetic model than other classical simulation methods. The method is then applied on data acquired over an unconsolidated aquifer located in Saint-Lambert-de-Lauzon, Quebec, Canada. The data set consists of measurements from (i) GPR crosshole acquisition, (ii) cone penetration testing with pressure measurement combined with soil moisture resistivity, and (iii) a borehole electromagnetic flowmeter. By using the presented Bayesian approach, we generated multiple hydraulic conductivity realizations that are in good agreement with the hydrogeological model of the area. </jats:p

Non-Gaussian Gas Hydrate Grade Simulation at the Mallik Site, Mackenzie Delta, Canada

For the past decades, gas hydrate reservoirs have beneficiated from an increasing attention in the academic and industrial worlds. As a result, there is a growing need to develop specific and comprehensive gas hydrate reservoir characterization methods. This study explores the use of a stochastic Bayesian algorithm to integrate well-logs and 3D acoustic impedance in order to estimate gas hydrate grades (product of saturation and total porosity) over a representative volume of the Mallik gas hydrate field, located in the Mackenzie Delta, Northwest Territories of Canada. First, collocated log data from boreholes Mallik 5L-38 and 2L-38 are used to estimate the statistical relationship between acoustic impedance and gas hydrate grades. Second, conventional stochastic Bayesian simulation is applied to generate multiple gas hydrate grade 3D fields integrating log data and lateral variability of 3D acoustic impedance. These equiprobable scenarios permit to quantify the uncertainty over the estimation, and identify zones where this uncertainty is greater. Contrary to conventional stochastic reservoir modeling workflows, the proposed method allows integrating non Gaussian and non linear distributions. This permits to handle bimodal distributions without using complex stochastic transforms. The results present gas hydrate grade values that are in accordance with well-log data. The relatively low standard deviation calculated at each pixel using all realizations suggests that gas hydrate grades is well explained by acoustic impedance and log data