بازسازی تصویر سنگ مخزن متراکم با شبکه عصبی مولد رقابتی

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی نفت، دانشکده مهندسی شیمی، دانشگاه تربیت مدرس، تهران، ایران

چکیده

بخش قابل توجهی از منابع هیدروکربنی ایران از مخازن شکاف‌دار با ماتریس سنگ متراکم تولید می‌شود. ساختار حفرات این مخازن، پیچیدگی‌های زیادی دارد و حفرات و گلوگاه‌های ریز در ابعاد نانومتری ذخیره هیدروکربن را به‌عهده دارند. با درک ساختار فضای متخلخل و بررسی جریان سیال درون حفرات ریز می‌توان دید بهتری از رفتار فضای متخلخل در مقیاس بزرگ به‌دست آورد. بررسی جریان سیال در سنگ مخزن نیازمند ساختارهای سه‌بعدی با دقت مناسب است. با این وجود استفاده از روش‌های مرسوم برای بازسازی شبکه حفرات پرهزینه است و از طرفی با پیچیده‌تر شدن این ساختارها توانایی این روش‌ها در بازسازی شبکه حفرات به‌طور چشم‌گیری کاهش می‌یابد. در سال‌های اخیر با پیشرفت در علوم کامپیوتر به ویژه هوش مصنوعی دروازه جدیدی به‌منظور بازسازی ساختارهای پیچیده به مانند سنگ مخزن گشوده شده است. با استفاده از روش‌های یادگیری ماشین می‌توان مدل‌های سه‌بعدی با دقت بسیار بالا ایجاد و خواص پتروفیزیکی سنگ را از آن‌ها محاسبه کرد. یکی از این روش‌ها شبکه عصبی مولد رقابتی می باشد که توانایی خود در بازسازی شبکه‌ حفرات را ثابت کرده است. در این پژوهش، از یک شبکه عصبی مولد رقابتی با لایه‌های همگشتی به‌منظور بازسازی تصاویر FIB-SEM یک سنگ مخزن متراکم در مقیاس حفره استفاده شده است. با استفاده از شبکه عصبی آموزش داده شده، تحقق‌های مختلفی از شبکه‌ حفرات ساخته می‌شود. تخلخل و تراوایی تصاویر باز ساخته شده بسیار نزدیک به این خواص در نمونه تصویر واقعی بوده و دارای انحراف به‌ترتیب 07/1 و 24/5% برای تخلخل و تراوایی است. مشاهده می‌شود که شبکه عصبی مولد رقابتی تونایی بالایی در بازسازی شبکه حفرات دارد و می‌توان با کمک آن به بررسی شرایط سنگ مخزن در مقیاس حفره پرداخت.
 

کلیدواژه‌ها


عنوان مقاله [English]

Pore-scale Reconstruction of Tight Reservoirs Using Generative Adversarial Networks

نویسندگان [English]

  • Ali Karimi
  • Saeid Sadeghnejad
Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
چکیده [English]

A significant amount of Iranian hydrocarbon resources is produced from fractured reservoirs with tight rock matrices. The structure of pores in these reservoirs is so complex. Very tiny pores and throats in nanometer sizes are responsible for reserving hydrocarbons. By understanding the structure of porous media and examining fluid flow inside these nanometer pores, we can better understand the porous media›s behaviour on larger scales. Investigating fluid flow in reservoir rocks requires three-dimensional structures with appropriate accuracy. However, using conventional methods to reconstruct a porous medium is expensive. On the other hand, as these structures become more complex, the ability of these methods to reconstruct pore network models decreases significantly. In recent years, with the advance in computer science, especially artificial intelligence, a new gate has been opened for reconstructing complex structures such as tight reservoir rocks. By implementing machine learning methods, three-dimensional pore-scale models can be created with high accuracy. The petrophysical properties of rocks can be calculated from them. One of these methods is the generative adversarial network (GAN), which has proven to reconstruct the pore structure of rocks. This study uses a GAN with convolutional layers to reconstruct the images obtained from FIB-SEM of a tight reservoir rock at the pore scale. Different realizations of the pore space are reconstructed by the trained GAN. The porosity and permeability of the reconstructed images are very close to the properties in the actual FIB-SEM image and have a deviation of 1.07% and 5.24%, respectively. It can be seen that GANs have a high capacity in rock reconstruction at the pore scale, especially for tight reservoirs.
 

کلیدواژه‌ها [English]

  • Image Reconstruction
  • Pore Network Modelling
  • Generative Adversarial Networks
  • Tight Reservoir Rock
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