پیش بینی سرعت موج برشی با استفاده از لاگ های پتروفیزیکی و الگوریتم های یادگیری عمیق در یکی از میادین هیدروکربنی ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه اکتشاف نفت، دانشکده مهندسی معدن، دانشکدگان فنی دانشگاه تهران، دانشگاه تهران، تهران، ایران

2 گروه اکتشاف نفت، دانشکده مهندسی نفت، دانشکدگان فنی دانشگاه تهران، دانشگاه تهران، تهران، ایران

چکیده

چکیده
سرعت موج برشی یکی از مهمترین پارامترهای اثرگذار در مدل سازی های پتروفیزیکی و ژئومکانیکی است. برای تعیین سرعت موج برشی مدل های تجربی زیادی معرفی شده اند که هر کدام از آنها مختص منطقه ای خاص هستند. یکی از روش هایی که اخیرا زیاد مورد استفاده قرار می گیرد روش های هوشمند است.
در این مطالعه سرعت موج برشی با استفاده از روش یادگیری عمیق در یکی از چاه های مخازن هیدروکربنی جنوب غرب ایران پیش بینی شده است. در این مقاله از ضریب همبستگی پیرسون برای انتخاب ویژگی ها استفاده شد و در ادامه با استفاده از شبکه عصبی پرسپترون چندلایه (MLP)، شبکه عصبی بازگشتی + شبکه عصبی پرسپترون چندلایه (LSTM+MLP)، شبکه‌ عصبی تبدیلی + شبکه عصبی پرسپترون چندلایه (CNN+MLP) سرعت موج برشی تخمین زده شد و مقدار خطا و ضریب تعیین (R2) برای داده های آموزش و تست محاسبه گردید. همچنین جهت اطمینان از نتایج الگوریتم ها بخشی از داده به عنوان داده شاهد کنار گذاشته شد و خطا و ضریب تعیین برای این داده ها نیز محاسبه گردید که ضریب تعیین
RMLP^2=0.7989، R(LSTM+MLP)^2=0.8984، R(CNN+MLP)^2=0.9032 به دست آمده است. نتایج بیانگر بالاتر بودن خطا و کمتر بودن ضریب تعیین مربوط به شبکه MLP نسبت به شبکه های LSTM+MLP و CNN+MLP است. با توجه به نتایج حاصل شده می توان از روش های یادگیری عمیق جهت پیش بینی سرعت موج برشی به عنوان روشی مناسب و کم هزینه استفاده کرد.
واژه های کلیدی: سرعت موج برشی، داده های پتروفیزیکی، لاگ DSI، مخزن آسماری، یادگیری عمیق

کلیدواژه‌ها

موضوعات


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