STOCHASTIC METHODS FOR SURFACTANT-POLYMER FLOODING AND WELL PLACEMENT OPTIMIZATION
Abeeb Awotunde, Ahmed Hassaan
كلية هندسة البترول وعلوم الأرض-جامعة الملك فهد للبترول والمعادن · السعودية
Chemical flooding is one of the most important enhanced oil recovery (EOR) techniques for the current decade and a great number of fields in the world have been produced with chemical injection into the reservoir. There has been a revival in chemical enhanced oil recovery techniques during the last few years because of the advancements in technology and high oil prices. Also with the depletion of resources, there exists a need to efficiently design production strategies with effective EOR mechanisms. Surfactant-Polymer flooding in one of the successful EOR techniques which alters the wettability of the rock and controls the mobility of the liquids. On the other hand, well placement is one of the main steps in field development plan. A well planned EOR process can be spoiled if right candidate wells are not selected for the EOR process. Therefore the selection and optimization of EOR process in conjunction with well placement optimization is needed for effective ultimate recovery process. The main objective of enhanced oil recovery is to recover as much oil from the reservoir as possible within economic limits. The maximum oil that can be recovered from the reservoir can be evaluated using ultimate recovery factor (URF) while net present value (NPV) serves as the economic indicator all over the project life. This research uses Surfactant-Polymer (SP) flooding as the chemical EOR process. The objective of the research is to select the best stochastic optimization technique for the Surfactant-Polymer flooding process with well placement optimization. It includes the simulation of SP flooding process for different scenarios having net present value (NPV) and ultimate recovery factor (URF) as the objective functions while the time for water-flooding, surfactant flooding, polymer flooding, surfactant and polymer concentrations in injection wells, and well locations served as the optimized variables. The simulation and optimization study has been done using Eclipse reservoir simulator and MATLAB. The results indicate that stochastic optimization techniques can be used to find the optimal combination of operational parameters that give high Net Present Value (NPV) and Ultimate Recovery (UR).