Receding horizon optimization of petroleum reservoir waterflooding using sequential quadratic programming

In this paper, optimization of reservoir waterflooding process was studied using three different strategies; static optimization, and two forms of receding horizon control (RHC) which are moving-end and fixed-end RHC. MATLAB Reservoir Simulator (MRST) from SINTEF was used for reservoir simulation. The objective function to be maximized is net present value (NPV) of the venture while the control variable is water injection rate. Sequential Quadratic Programming (SQP) algorithm was applied to solve the optimization problem. The SQP solver is integrated with a genetic algorithm (GA) solver to search for the globally optimal starting point so that the static optimization is not tripped at a local minimum. The globally optimal starting point obtained was then used for RHC algorithm. It was found out that moving-end RHC gave the highest NPV with an increase of 15.07% over static optimization case. The improvement is as a result of early accelerated oil production and drastic reduction in water injection for subsequent years which was followed by a total shut-in after 6.07 years. The increase in NPV obtained by fixed-end RHC is just about 2.83% comparing with static optimization. However, it was noted that the effectiveness of moving-end RHC can be further appreciated if a variable optimization window is used with a stopping criterion such as a point of zero cash flow instead of the fixed twenty-year production period. Next step research will focus on online control design to implement the RHC strategy.