Method for exploiting a geological reservoir according to an exploitation scheme defined on the basis of a reservoir model, said reservoir model comprising a mesh associated with parameters θ of said reservoir, characterised in that the following steps are carried out: a) a reservoir model matched to data measured within said reservoir is constructed by means of the following steps: i) stochastically, an initial set of reservoir models is generated on the basis of probability distributions p ( θ) of said parameters θ; ii) for each reservoir model of the set, an objective function F ( θ) measuring a deviation between n dynamic data y 1,...,y n acquired during exploitation and dynamic data simulated by means of a flow simulator and of said reservoir models belonging to said set, is determined; iii) on the basis of said objective function F ( θ) determined for each reservoir model of the set, an analytical conditional-probability distribution p̃ ( θ |y i,...,y n) of the parameters θ is determined, knowing said measured dynamic data y i,...,y n, by implementing the following steps: the approximation of a function G ( θ) is determined by computing G ( θ j) = exp ( -F ( θ j)) for the j models belonging to said set, θ j being an uncertain parameter, then by interpolating said values of G ( θ j); said analytical distribution is computed on the basis of the following relationship: p ̃ θ | y 1,..., y n = G θ × p θ ∫ G θ × p θ dθ; iv) at least one new reservoir model is generated by means of said analytical distribution p̃ ( θ | y 1,..., y n) and said new model is added to said set of reservoir models; v) steps ii) to iv) are reiterated for said set of reservoir models and the reservoir model that minimizes said objective function is determined; b) an optimal exploitation scheme is determined for the reservoir by simulating the exploitation of the reservoir by means of said matched reservoir model and of said flow simulator; and c) said reservoir is exploited by implementing said optimal exploitation scheme. a) a reservoir model matched to data measured within said reservoir is constructed by means of the following steps: i) stochastically, an initial set of reservoir models is generated on the basis of probability distributions p ( θ) of said parameters θ; ii) for each reservoir model of the set, an objective function F ( θ) measuring a deviation between n dynamic data y 1,...,y n acquired during exploitation and dynamic data simulated by means of a flow simulator and of said reservoir models belonging to said set, is determined; iii) on the basis of said objective function F ( θ) determined for each reservoir model of the set, an analytical conditional-probability distribution p̃ ( θ |y i,...,y n) of the parameters θ is determined, knowing said measured dynamic data y i,...,y n, by implementing the following steps: the approximation of a function G ( θ) is determined by computing G ( θ j) = exp ( -F ( θ j)) for the j models belonging to said set, θ j being an uncertain parameter, then by interpolating said values of G ( θ j); said analytical distribution is computed on the basis of the following relationship: p ̃ θ | y 1,..., y n = G θ × p θ ∫ G θ × p θ dθ; iv) at least one new reservoir model is generated by means of said analytical distribution p̃ ( θ | y 1,..., y n) and said new model is added to said set of reservoir models; v) steps ii) to iv) are reiterated for said set of reservoir models and the reservoir model that minimizes said objective function is determined; i) stochastically, an initial set of reservoir models is generated on the basis of probability distributions p ( θ) of said parameters θ; ii) for each reservoir model of the set, an objective function F ( θ) measuring a deviation between n dynamic data y 1,...,y n acquired during exploitation and dynamic data simulated by means of a flow simulator and of said reservoir models belonging to said set, is determined; iii) on the basis of said objective function F ( θ) determined for each reservoir model of the set, an analytical conditional-probability distribution p̃ ( θ |y i,...,y n) of the parameters θ is determined, knowing said measured dynamic data y i,...,y n, by implementing the following steps: the approximation of a function G ( θ) is determined by computing G ( θ j) = exp ( -F ( θ j)) for the j models belonging to said set, θ j being an uncertain parameter, then by interpolating said values of G ( θ j); said analytical distribution is computed on the basis of the following relationship: p ̃ θ | y 1,..., y n = G θ × p θ ∫ G θ × p θ dθ; the approximation of a function G ( θ) is determined by computing G ( θ j) = exp ( -F ( θ j)) for the j models belonging to said set, θ j being an uncertain parameter, then by interpolating said values of G ( θ j); said analytical distribution is computed on the basis of the following relationship: p ̃ θ | y 1,..., y n = G θ × p θ ∫ G θ × p θ dθ; iv) at least one new reservoir model is generated by means of said analytical distribution p̃ ( θ | y 1,..., y n) and said new model is added to said set of reservoir models; v) steps ii) to iv) are reiterated for said set of reservoir models and the reservoir model that minimizes said objective function is determined; b) an optimal exploitation scheme is determined for the reservoir by simulating the exploitation of the reservoir by means of said matched reservoir model and of said flow simulator; and c) said reservoir is exploited by implementing said optimal exploitation scheme.