Semiparametric Approach to Hammerstein System Identification
Abstract Two semiparametric algorithms to recover a nonlinear characteristic in a Hammerstein system are proposed. Both are obtained by incorporating a parametric component into the kernel nonparametric algorithm. For small number of observations, their identification errors are smaller than that of the purely nonparametric algorithm. The same idea is also proposed for identification of linear dynamic component. Parametric instrumental variables estimate is elastically substituted by the nonparametric correlation-based method, when the number of observations tends to infinity.
Summary In the pilot study, a new geomodeling technology is presented for developing 3D heterogonous geomodels for Athabasca McMurray (MCMR) oil sands. The geomodels were used to simulate SAGD thermal recovery. Both conventional SAGD completions and SAGD with ICD completions were tested. The simulation results show that for a heterogeneous Athabasca McMurray oil sand reservoir, the ICD completion technology enhances oil production and reduces steam consumption. As a result, the economics of SAGD recovery from the heterogeneous sands is improved.