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Renard, Philippe

Nom
Renard, Philippe
Affiliation principale
Site web
Fonction
Directeur de Recherche
Email
Philippe.Renard@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/150
_
0000-0003-4504-435X

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  • Publication
    Accès libre
    Blocking Moving Window algorithm: Conditioning multiple?point simulations to hydrogeological data
    (2010-1-10)
    Alcolea, Andres
    ;
    Renard, Philippe 
    Connectivity constraints and measurements of state variables contain valuable information on aquifer architecture. Multiple‐point (MP) geostatistics allow one to simulate aquifer architectures, presenting a predefined degree of global connectivity. In this context, connectivity data are often disregarded. The conditioning to state variables is usually carried out by minimizing a suitable objective function (i.e., solving an inverse problem). However, the discontinuous nature of lithofacies distributions and of the corresponding objective function discourages the use of traditional sensitivity‐based inversion techniques. This work presents the Blocking Moving Window algorithm (BMW), aimed at overcoming these limitations by conditioning MP simulations to hydrogeological data such as connectivity and heads. The BMW evolves iteratively until convergence: (1) MP simulation of lithofacies from geological/geophysical data and connectivity constraints, where only a random portion of the domain is simulated at every iteration (i.e., the blocking moving window, whose size is user‐defined); (2) population of hydraulic properties at the intrafacies; (3) simulation of state variables; and (4) acceptance or rejection of the MP simulation depending on the quality of the fit of measured state variables. The outcome is a stack of MP simulations that (1) resemble a prior geological model depicted by a training image, (2) honor lithological data and connectivity constraints, (3) correlate with geophysical data, and (4) fit available measurements of state variables well. We analyze the performance of the algorithm on a 2‐D synthetic example. Results show that (1) the size of the blocking moving window controls the behavior of the BMW, (2) conditioning to state variable data enhances dramatically the initial simulation (which accounts for geological/geophysical data only), and (3) connectivity constraints speed up the convergence but do not enhance the stack if the number of iterations is large.
  • Publication
    Accès libre
    Reducing the impact of a desalination plant using stochastic modeling and optimization techniques
    (2009)
    Alcolea, Andres
    ;
    Renard, Philippe 
    ;
    Mariethoz, Grégoire 
    ;
    Bertone, François
    Water is critical for economic growth in coastal areas. In this context, desalination has become an increasingly important technology over the last five decades. It often has environmental side effects, especially when the input water is pumped directly from the sea via intake pipelines. However, it is generally more efficient and cheaper to desalt brackish groundwater from beach wells rather than desalting seawater. Natural attenuation is also gained and hazards due to anthropogenic pollution of seawater are reduced. In order to minimize allocation and operational costs and impacts on groundwater resources, an optimum pumping network is required. Optimization techniques are often applied to this end. Because of aquifer heterogeneity, designing the optimum pumping network demands reliable characterizations of aquifer parameters. An optimum pumping network in a coastal aquifer in Oman, where a desalination plant currently pumps brackish groundwater at a rate of 1200 m3/h for a freshwater production of 504 m3/h (insufficient to satisfy the growing demand in the area) was designed using stochastic inverse modeling together with optimization techniques. The Monte Carlo analysis of 200 simulations of transmissivity and storage coefficient fields conditioned to the response to stresses of tidal fluctuation and three long term pumping tests was performed. These simulations are physically plausible and fit the available data well. Simulated transmissivity fields are used to design the optimum pumping configuration required to increase the current pumping rate to 9000 m3/h, for a freshwater production of 3346 m3/h (more than six times larger than the existing one). For this task, new pumping wells need to be sited and their pumping rates defined. These unknowns are determined by a genetic algorithm that minimizes a function accounting for: (1) drilling, operational and maintenance costs, (2) target discharge and minimum drawdown (i.e., minimum aquifer vulnerability) and (3) technical feasibility of the solution. The performance of the optimum pumping network is compared to that of a synthetic, tradition-based hand-delineated design, where optimization is not performed. Results show that the combined use of stochastic inverse modeling and optimization techniques leads to minimum side effects (e.g., drawdowns in the area are reduced substantially) and to a significant reduction of allocation and operational costs.Water is critical for economic growth in coastal areas. In this context, desalination has become an increasingly important technology over the last five decades. It often has environmental side effects, especially when the input water is pumped directly from the sea via intake pipelines. However, it is generally more efficient and cheaper to desalt brackish groundwater from beach wells rather than desalting seawater. Natural attenuation is also gained and hazards due to anthropogenic pollution of seawater are reduced. In order to minimize allocation and operational costs and impacts on groundwater resources, an optimum pumping network is required. Optimization techniques are often applied to this end. Because of aquifer heterogeneity, designing the optimum pumping network demands reliable characterizations of aquifer parameters. An optimum pumping network in a coastal aquifer in Oman, where a desalination plant currently pumps brackish groundwater at a rate of 1200 m3/h for a freshwater production of 504 m3/h (insufficient to satisfy the growing demand in the area) was designed using stochastic inverse modeling together with optimization techniques. The Monte Carlo analysis of 200 simulations of transmissivity and storage coefficient fields conditioned to the response to stresses of tidal fluctuation and three long term pumping tests was performed. These simulations are physically plausible and fit the available data well. Simulated transmissivity fields are used to design the optimum pumping configuration required to increase the current pumping rate to 9000 m3/h, for a freshwater production of 3346 m3/h (more than six times larger than the existing one). For this task, new pumping wells need to be sited and their pumping rates defined. These unknowns are determined by a genetic algorithm that minimizes a function accounting for: (1) drilling, operational and maintenance costs, (2) target discharge and minimum drawdown (i.e., minimum aquifer vulnerability) and (3) technical feasibility of the solution. The performance of the optimum pumping network is compared to that of a synthetic, tradition-based hand-delineated design, where optimization is not performed. Results show that the combined use of stochastic inverse modeling and optimization techniques leads to minimum side effects (e.g., drawdowns in the area are reduced substantially) and to a significant reduction of allocation and operational costs.