مهندسی شیمی ایران

مهندسی شیمی ایران

بهینه‌سازی شرایط عملیاتی چاه‌های ازدیاد برداشت نفت با مدل جایگزین ‌و الگوریتم ژنتیک چندهدفه: تزریق آب کم‌شور

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی کارشناسی ارشد مهندسی نفت، دانشگاه صنعتی تبریز
2 استاد مهندسی نفت، دانشگاه صنعتی تبریز
چکیده
بخش قابل‌توجهی از ذخایر نفت و گاز جهان در سنگ‌های کربناته واقع‌شده‌است. مطالعات اخیر نشان‌داده‌است که کاهش شوری آب تزریقی می‌تواند تولید نفت را به‌طور قابل‌توجهی افزایش‌دهد؛ بااین‌حال، بهینه‌سازی شرایط عملیاتی چاه‌ها درحین تزریق آب کم‌شور به‌دلیل پیچیدگی‌های فرایند و زمان‌بر بودن شبیه‌سازی‌ها، یک چالش اساسی محسوب‌می‌شود. این مطالعه برپایۀ داده‌های شبیه‌سازی‌شده از یک مخزن کربناتۀ مصنوعی انجام‌شده‌است که خصوصیات آن با داده‌های واقعی میدان‌های جنوب ایران تنظیم‌شده‌است. در این مطالعه، برخلاف تحقیقات پیشین، از ترکیب یک مدل جایگزین مبتنی‌بر یادگیری ماشین با الگوریتم ژنتیک چندهدفه برای شبیه‌سازی و بهینه‌سازی هم‌زمان محدودیت‌های عملیاتی چاه‌ها، شامل: حداکثر دبی تولید، حداقل فشار ته‌چاهی، دبی تزریق آب و وضعیت باز یا بسته بودن مشبک‌کاری‌ها استفاده‌شد. مدل جایگزین با دقت مناسب (ضریب همبستگی 0/989 برای داده‌های آموزش و 0/984 برای آزمون) موجب تسریع فرایند شبیه‌سازی شد. سپس، با درنظرگرفتن ارزش خالص فعلی، ضریب بازیافت نفت و برش آب به‌عنوان مشخصه‌‌های هدف، مجموعه‌ای از راه‌حل‌های بهینه در قالب جبهۀ پارتو به‌دست‌آمد. نتایج نشان‌داد که انتخاب شرایط عملیاتی بهینه می‌تواند منجربه افزایش 135 میلیون دلاری در ارزش خالص فعلی و دستیابی‌به ضریب بازیافت نفت تا 65/54 درصد شود.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Optimization of EOR Well Constraints Using Surrogate Modelsand NSGA II: Low-Salinity Water Flooding

نویسندگان English

K. Mohammadzadeh 1
S. A. R. Tabatabaei-Nezhad 2
1 MSc. Student of Petroleum Engineering, Tabriz University of Technology
2 Professor of Petroleum Engineering, Tabriz University of Technology
چکیده English

A significant portion of the world's oil reserves is hosted in carbonate formations. Recent studies on low-salinity water injection have shown that reducing the salinity of the injected water can significantly enhance oil recovery. However, optimizing well operating conditions during low-salinity water injection remains a major challenge due to the process complexity and the computational cost of reservoir simulations.
Unlike previous works, this study employed a combination of a machine-learning-based surrogate model and a multi-objective genetic algorithm to simultaneously model and optimize well operating constraints, including maximum oil production rate, minimum bottom-hole pressure, water injection rate, and perforation status (open/closed). The developed surrogate model demonstrated high accuracy (R² = 0.989 for training and 0.984 for testing) and significantly reduced the simulation time. Subsequently, by considering net present value, oil recovery factor, and water cut as objective functions, a set of optimal solutions was obtained on the Pareto front. The results indicated that optimal well conditions could lead to a $135 million increase in net present value and an oil recovery factor of up to 65.54%.

کلیدواژه‌ها English

Low Salinity Water Injection
Carbonate Oil Reservoir
Net Present Value
Multi-Objective Genetic Algorithm
Well-Operating Condition
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