نشریه ژئومکانیک و ژئوانرژی

نشریه ژئومکانیک و ژئوانرژی

ارزیابی نفوذپذیری نسبی نفت و آب با استفاده از یادگیری ماشین: مطالعه موردی از جنوب‌غرب ایران

نوع مقاله : مقاله پژوهشی

نویسندگان
علی رنجبر 1
محمد رسول دهقانی 2
مهدی مالکی 3
1 دانشگاه خلیج فارس، دانشکده مهندسی نفت، گاز و پتروشیمی، گروه مهندسی نفت
2 دانشگاه شهید باهنر
3 دانشگاه خلیج فارس
10.22107/ggj.2025.559197.1260
چکیده
نفوذپذیری نسبی یکی از پارامترهای پتروفیزیکی کلیدی است که رفتار جریان چندفازی در محیط متخلخل را کنترل کرده و تأثیر قابل‌توجهی بر دقت شبیه‌سازی مخزن، پیش‌بینی برداشت و برنامه‌ریزی ازدیاد برداشت دارد. روش‌های آزمایشگاهی تعیین این پارامتر دقیق ولی زمان‌بر و پرهزینه‌اند. این پژوهش با بهره‌گیری از تکنیک‌های یادگیری ماشین و داده‌های مغزه‌ای از یک مخزن در جنوب‌غرب ایران، به برآورد نفوذپذیری نسبی نفت و آب پرداخته است. شانزده نمونه مغزه با هفت ویژگی ورودی برای آموزش چهار مدل Extra Trees، K-Nearest Neighbors، Categorical Boosting و Extreme Gradient Boosting استفاده شد. مدل‌ها با روش Bayesian Hyperparameter Tuning بهینه و با شاخص‌های R²، RMSE و MAE ارزیابی شدند. در برآورد نفوذپذیری نسبی آب، مدل Extra Trees بهترین عملکرد را با R² = 0.9974، RMSE = 0.0045 و MAE = 0.0007 نشان داد. مدل KNN نیز به‌ویژه در بازه نفوذپذیری 0.1 تا 0.2 عملکرد مطلوبی داشت. در برآورد نفوذپذیری نسبی نفت، مدل KNN دقیق‌ترین نتایج را با R² = 0.9973، RMSE = 0.0113 و MAE = 0.0024 ارائه کرد، در حالی که مدل Extra Trees در محدوده‌های بالای نفوذپذیری ضعیف‌تر عمل کرد. تحلیل حساسیت SHAP نشان داد اشباع آب مؤثرترین عامل در هر دو مدل است؛ نفوذپذیری نفت در اشباع آب اولیه برای مدل آب و ویسکوزیته نفت برای مدل نفت اهمیت بالایی دارند. نتایج نشان می‌دهد یادگیری ماشین روشی توانمند و کارآمد برای جایگزینی آزمون‌های آزمایشگاهی در برآورد نفوذپذیری نسبی است و انتخاب مدل به نوع سیال هدف وابسته بوده و می‌تواند موجب بهبود شبیه‌سازی، کاهش هزینه‌ها و مدیریت بهینه مخزن شود.
کلیدواژه‌ها
نفوذپذیری نسبی
CatBoost
آنالیز مغزه
Extra trees
تحلیل SHAP

عنوان مقاله English

Estimating Oil-Water Relative Permeability Using Machine Learning: A Case Study from Southwest Iran

نویسندگان English

Ali Ranjbar 1
Mohammad Rasul Dehghani 2
Mahdi Maleki 3
1 Faculty of Petroleum, Gas and Petrochemical Engineering, Petroleum Engineering Department,, Persian Gulf University, Bushehr, Iran
2 Shahid Bahonar University
3 Persian Gulf University
چکیده English

Relative permeability is a critical petrophysical parameter that controls multiphase flow behavior in porous media and significantly impacts reservoir simulation accuracy, recovery forecasting, and EOR planning. Conventional laboratory-based methods for determining relative permeability, while accurate, are time-intensive, costly, and spatially limited. This study focuses on applying machine learning techniques to estimate oil and water relative permeability using core data from a reservoir in southwest Iran. Sixteen core samples were analyzed, and seven input features—absolute permeability, porosity, irreducible water saturation, oil permeability at Swi, viscosities of oil and water, and pressure differential—were used to train four machine learning models: Extra Trees, K-Nearest Neighbors, Categorical Boosting, and Extreme Gradient Boosting.Models were optimized using Bayesian hyperparameter tuning and evaluated using R², RMSE, and MAE. For water relative permeability, the Extra Trees model delivered the best performance, achieving an R² of 0.9974 on the overall dataset, with the lowest RMSE (0.0045) and MAE (0.0007), indicating high accuracy and generalizability. KNN also performed well, especially in the 0.1–0.2 permeability range. In contrast, for oil relative permeability, the KNN model achieved the highest accuracy with an R² of 0.9973 and the lowest error metrics (RMSE = 0.0113, MAE = 0.0024), outperforming the other methods in both training and testing sets. Extra Trees performed poorly for oil permeability, especially in capturing higher permeability ranges. SHAP sensitivity analysis revealed that water saturation is the most influential factor for both oil and water models. For water, oil permeability at Swi also had a major impact; for oil, oil viscosity played the second most critical role.Overall, this study demonstrates that machine learning offers a robust, efficient alternative to laboratory experiments for estimating relative permeability, with model selection varying depending on the target fluid. These data-driven models can enhance reservoir simulation, reduce costs, and support improved reservoir management in heterogeneous systems.

کلیدواژه‌ها English

Relative permeability
Machine learning
Core analysis
Reservoir simulation
artificial intelligence
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نشریه ژئومکانیک و ژئوانرژی
دوره 8، شماره 1
بهار 1404
صفحه 67-81