دانلود مقاله ISI انگلیسی شماره 98179
ترجمه فارسی عنوان مقاله

بهینه سازی فرآیند حبه و پف در مقیاس میکروسکوپی با ترکیب آزمایش آزمایشگاهی و شبیه سازی عددی

عنوان انگلیسی
Huff and puff process optimization in micro scale by coupling laboratory experiment and numerical simulation
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
98179 2018 13 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Fuel, Volume 224, 15 July 2018, Pages 289-301

ترجمه کلمات کلیدی
حبه و پف بازیابی نفت پیشرفته، بهینه سازی ذرات ذرات، برنامه نویسی ژنتیک،
کلمات کلیدی انگلیسی
Huff and puff; Enhanced oil recovery; Particle swarm optimization; Genetic programming;
پیش نمایش مقاله
پیش نمایش مقاله  بهینه سازی فرآیند حبه و پف در مقیاس میکروسکوپی با ترکیب آزمایش آزمایشگاهی و شبیه سازی عددی

چکیده انگلیسی

Huff and Puff Enhanced Oil Recovery method can be regarded as promising process to increase oil production rates from developed field. Worldwide experiences in the application for an industrial-scale of this technology has been extensively discussed for heavy oil and tight oil production, however, field unique does not guarantee success for technology transfer to different site. In this way reservoir simulation is used as a first approximation of the project efficiency. However, numerical simulation requires representative data from laboratory experiments. Furthermore, huff-and-puff should be considered as complex problem, where influences from injection rates, soaking time and production rates can not be neglected. On the other side, conducting laboratory investigations are expensive and time-consuming, therefore, these researches should provide the most valuable information. In the presented methodology, laboratory experiments were conjuncted with the numerical representation of a core sample, to generate trustworthy models which were used for the process optimization. The optimal huff-n-puff operational design was computed using a stochastic population-based particle swarm optimization (PSO) method. As a consequence of high computational cost of a single full physic numerical run, the genetic programming as a novel tool for the huff-and-puff process optimization was successfully implemented. The comparison of the optimized results between genetic programming data-drive model and the full-physic numerical run revealed the right approximation and significant computing time reduction.