نشریه ژئومکانیک و ژئوانرژی

نشریه ژئومکانیک و ژئوانرژی

مروری بر کاربرد یادگیری ماشین در پیش‌بینی مخاطرات و مشکلات حفاری

نوع مقاله : مقاله مروری

نویسندگان
علی رنجبر 1
پریرخ ابراهیمی 2
1 دانشگاه خلیج فارس، دانشکده مهندسی نفت، گاز و پتروشیمی، گروه مهندسی نفت
2 دانشگاه خلیج فارس
10.22107/jpg.2024.421270.1222
چکیده
هزینه بالای عملیات حفاری منجر به افزایش چالش‌ها در بهینه‌سازی عملیات حفاری شده است. رمز موفقیت در کاهش این هزینه‌ها طراحی برنامه چاه بر اساس پیش‌بینی مسائل و مشکلات بالقوه حفاری می‌باشد. در چند دهه گذشته، صنعت حفاری تمایل روز‌افزونی به یادگیری ماشین برای پیش‌بینی مشکلات حفاری نشان داده است. این مقاله، مروری جامع از مطالعات مرتبط با بکارگیری یادگیری ماشین در پیش‌بینی رویدادهای پر مخاطره حفاری را ارائه می‌کند. در هر مطالعه، الگوریتم‌های یادگیری ماشین، تعداد نقاط داده، پارامترهای ورودی و خروجی‌ به ماشین و عملکرد الگوریتم مربوطه از مطالعات پیشین استخراج شده است. علاوه بر این، محدودیت‌ها، شباهت‌های مطالعات در هر دسته خلاصه شده و مروری از ادبیات به همراه توصیه‌هایی برای توسعه مطالعات آینده ارائه شده است. این بررسی‌ها نشان می‌دهد که الگوریتم شبکه عصبی مصنوعی محبوب‌ترین روش در بین الگوریتم‌های یادگیری ماشین در مطالعات انجام شده است؛ این در حالی است که دیگر الگوریتم ‌ها نظیر الگوریتم ماشین بردار پشتیبان و جنگل تصادفی ممکن است عملکرد بهتری را در استخراج نتایج از خود نشان دهند. همچنین لازم به ذکر است، بسیاری از مدل ‌های هوشمند ارائه شده توسط پژوهشگران از نمونه‌ های محدود و برای شرایط خاص ارائه نتایج چنین مطالعاتی ممکن است قابل تعمیم نباشد.
کلیدواژه‌ها
مشکلات حفاری
گیر لوله
هرزروی گردش سیال حفاری
شکستگی در سازند
لرزش کابل حفاری
یادگیری ماشین

عنوان مقاله English

An Overview of the Application of Machine Learning in Solving Drilling Problems

نویسندگان English

Ali Ranjbar 1
Parirokh Ebrahimi 2
1 Faculty of Petroleum, Gas and Petrochemical Engineering, Petroleum Engineering Department,, Persian Gulf University, Bushehr, Iran
2 Persian Gulf University
چکیده English

The high cost of drilling operations has led to increasing challenges in optimizing drilling operations. The key to success in reducing these costs is designing the well program based on the prediction of potential drilling issues and problems. Over the past few decades, the drilling industry has shown an increasing interest in machine learning to predict drilling problems. This paper presents a comprehensive review of studies related to the application of machine learning in predicting drilling hazard events. In each study, machine learning algorithms, the number of data points, input and output parameters to the machine and the performance of the corresponding algorithm are extracted from previous studies. In addition, limitations, similarities of the studies in each category are summarized and a review of the literature is provided along with recommendations for the development of future studies. These reviews show that the artificial neural network algorithm is the most popular method among the machine learning algorithms in the studies; Meanwhile, other algorithms such as support vector machine algorithm and random forest may show better performance in extracting results. It should also be noted that many of the intelligent models presented by researchers are based on limited samples and the specific conditions and may not be generalizable.

کلیدواژه‌ها English

Drilling problems
Pipe stick
Circulation loss
Fractures
Kick
Machine learning
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نشریه ژئومکانیک و ژئوانرژی
دوره 7، شماره 1
بهار 1403
صفحه 25-51