An iterative dynamic programming optimization based on biorthogonal spatial-temporal Hammerstein modeling for the enhanced oil recovery of ASP flooding

摘要

In this paper, an iterative dynamic programming (IDP) based on biorthogonal spatial-temporal Hammerstein modeling is developed to solve the enhanced oil recovery for alkali-surfactant-polymer (ASP) flooding. At first, the biorthogonal spatial-temporal Hammerstein model is presented to build the relation between the inputs and states, in which the Hammerstein model is expanded on a set of spatial basis functions and temporal basis functions. After inferring the necessary condition of solutions, these basis functions are determined by the snapshots method. Then, the least square estimation and singular value decomposition is used to identify the parameters in Hammerstein model. In addition, Auto-Regressive and Moving Average (ARMA) model is applied to build the model between states and outputs, whose parameters are identified by recursive least squares. At last, IDP algorithm is applied to solve the enhanced oil recovery problem for ASP flooding based on the identification model. Simulation verifies the accuracy and effectiveness of proposed method. (C) 2018 Elsevier Ltd. All rights reserved.