Integrated spatiotemporal modeling and mixed-integer approximate dynamic programming for ASP flooding

Abstract

Alkali–surfactant–polymer (ASP) flooding holds a critical position in oil development. This paper is concerned with the distributed parameter system (DPS) modeling and development scheme optimization of ASP flooding. Owing to the neglect of coupling among three-dimensional (3-D) spatial information, most of the existing data-driven modeling methods fail to provide a high-precision model for this type of strongly nonlinear and spatiotemporal coupling 3-D DPS. To overcome this shortcoming, an integrated spatiotemporal modeling method (ISM) is proposed, which reconstructs temporal–spatial relationship to avoid spatial mapping distortion based on canonical polyadic decomposition (CPD), and a spatiotemporal MSSO-LS-SVM algorithm is presented to compensate the nonlinear dynamics and decrease truncation error. Moreover, the synchronous optimization of ASP injection strategy and oil wells switching scheduling is an urgent industry requirement of intelligent oilfield decision system, which can be described a mixed-integer optimal control/dynamic programming problem. This paper first puts forward a mixed-integer approximate dynamic programming (MIADP) algorithm, dedicated for solving this class of optimal control problem with integer control subset requirements. In the MIADP, a parallel mixed-integer policy improvement framework is developed, where (i) the continuous control weight updates by the temporal difference (TD) error, (ii) the quantum evolution mechanism is designed to learn the integer control, and (iii) the two parts share information interactively and iterate to approach the optimal mixed-integer policy. At last, a case study on ASP flooding is taken, and the superior performance metrics illustrate the effectiveness of the proposed ISM method and MIADP algorithm.