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Selection of an Optimal Hybrid Water/Gas Injection Scenario for Maximization of Oil Recovery Using Genetic Algorithm

عنوان مقاله: Selection of an Optimal Hybrid Water/Gas Injection Scenario for Maximization of Oil Recovery Using Genetic Algorithm
شناسه ملی مقاله: JR_IJOGST-9-1_007
منتشر شده در شماره 1 دوره 9 فصل در سال 1399
مشخصات نویسندگان مقاله:

Shahin Kord - Assistant Professor, Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran
Omid Ourahmadi - M.S. Student, Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran
Arman Namaee-Ghasemi - M.S. Student, Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran

خلاصه مقاله:
Production strategy from a hydrocarbon reservoir plays an important role in optimal field development in the sense of maximizing oil recovery and economic profits. To this end, self-adapting optimization algorithms are necessary due to the great number of variables and the excessive time required for exhaustive simulation runs. Thus, this paper utilizes genetic algorithm (GA), and the objective function is defined as net present value (NPV). After developing a suitable program code and coupling it with a commercial simulator, the accuracy of the code was ensured using a synthetic reservoir. Afterward, the program was applied to an Iranian southwest oil reservoir in order to attain the optimum scenario for primary and secondary production. Different hybrid water/gas injection scenarios were studied, and the type of wells, the number of wells, well coordination/location, and the flow rate (production/injection) of each well were optimized. The results from these scenarios were compared, and simultaneous water and gas (SWAG) injection was found to have the highest overall profit representing an NPV of about 28.1 billion dollars. The application of automated optimization procedures gives rise to the possibility of including additional decision variables with less time consumption, and thus pushing the scopes of optimization projects even further.

کلمات کلیدی:
optimization, Production optimization, Well Placement, Genetic Algorithm

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1015940/