Optimal control of polymer flooding based on mixed-integer iterative dynamic programming

Polymer flooding is one of the most important technologies for enhanced oil recovery. In this article, a mixed-integer optimal control model of distributed parameter systems (DPS) for the injection strategies is established, which involves the performance index as maximum of the profit, the governing equations as the fluid flow equations of polymer flooding and some inequalities constraints, such as polymer concentration and injection amount limitation. The control variables are the volume size, the injection concentration of each slug and the terminal flooding time. For the constant injection rate, the slug size is determined by the integer time stage length, and thus the integer variables are introduced in the DPS. To cope with the optimal control problem (OCP) of this DPS, a mixed-integer iterative dynamic programming incorporating a special truncation procedure to handle integer restrictions on stage lengths is proposed. First, the OCP with variable time stage lengths is transformed into a fixed time stage problem by introducing a normalised time variable. Then, the optimisation procedure is carried out at each stage and preceded backwards in a systematic way. Finally, the numerical results of an example illustrate the effectiveness of the proposed method.

[1]  B. K. Maitin Performance Analysis of Several Polyacrylamide Floods in North German Oil Fields , 1992 .

[2]  Rein Luus,et al.  Handling Inequality Constraints in Optimal Control by Problem Reformulation , 2009 .

[3]  Yang Lei,et al.  Optimal control solving of polymer flooding based on real-coded genetic algorithm , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[4]  R. Luus Optimal control by dynamic programming using systematic reduction in grid size , 1990 .

[5]  Zhao Fulin,et al.  A Study on Mass Concentration Determination and Property Variations of Produced Polyacrylamide in Polymer Flooding , 2011 .

[6]  Zohreh Fathi,et al.  Use of optimal control theory for computing optimal injection policies for enhanced oil recovery , 1986, Autom..

[7]  John R. Fanchi Fundamentals of Reservoir Simulation , 2006 .

[8]  Hanqiao Jiang,et al.  The Influence of the Combination of Polymer and Polymer–Surfactant Flooding on Recovery , 2011 .

[9]  Louis J. Durlofsky,et al.  Implementation of Adjoint Solution for Optimal Control of Smart Wells , 2005 .

[10]  Yang Lei,et al.  Optimal control solving of polymer flooding based on a hybrid genetic algorithm , 2010, Proceedings of the 29th Chinese Control Conference.

[11]  Jan Dirk Jansen,et al.  Dynamic Optimization of Water Flooding with Smart Wells Using Optimal Control Theory , 2002 .

[12]  Karen Bybee,et al.  Evaluation of Polymer-Injection Projects in Brazil , 2006 .

[13]  Hanqiao Jiang,et al.  Study of Interfacial Tension Between Oil and Surfactant Polymer Flooding , 2010 .

[14]  Zohreh Fathi,et al.  Optimal injection policies for enhanced oil recovery: Part 1-Theory and computational strategies , 1984 .

[15]  W. F. Ramirez,et al.  Optimal Injection Policies for Enhanced Oil Recovery , 1984 .

[16]  Lei Yang Solution of optimal control of polymer flooding based on parallelization of iterative dynamic programming , 2009 .

[17]  Jan Dirk Jansen,et al.  Dynamic Optimization of Waterflooding With Smart Wells Using Optimal Control Theory , 2004 .

[18]  Rein Luus,et al.  Iterative dynamic programming , 2019, Iterative Dynamic Programming.

[19]  Liu Wei,et al.  Optimal Control of Steamflooding , 1993 .