معرفی دو روش مبتنی بر داده برای تعیین کیفیت رخساره‌های گازی در غرب استرالیا

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشجوی دکترا، دانشکده مهندسی معدن، دانشگاه تهران، تهران

2 استاد، دانشکده مهندسی معدن، دانشگاه تهران، تهران

چکیده

در تعیین نقاط بهینه حفاری‌های تولیدی مهم است که زون‌های با کیفیت مخزنی مشخص باشند. برای این منظور از داده‌های ژئوشیمیایی که معمولا تعداد آن‌ها کم است استفاده می‌کنند. این گسستگی داده‌ها باعث ایجاد شکاف اطلاعاتی می‌شود. چنانچه از داده‌هایی با پیوستگی بیشتر استفاده شود طوری که دقت مدل‌سازی مناسب باشد، حفاری‌ها با شانس بیشتری انجام می‌شود. در این مطالعه هدف بر این است تا کیفیت رخساره‌های گازی با استفاده از دو روش ناپارامتری آماری (پارزن) و تکنیک یادگیری عمیق با نظارت (شبکه عصبی بازگشتی حافظه کوتاه‌مدت بلند LSTM) و به کمک داده‌های نگار چاه و لرزه‌ای مدل شوند. همچنین در نظر است شبکه طراحی شده به وسیله دو روش بهینه‌سازی ابتکاری الگوریتم رقابت استعماری و الگوریتم نهنگ بهینه‌سازی ‌شود. نتایج حاصل از این مطالعه نشان می‌دهد که هر دو روش نتایج خوبی در طبقه‌بندی دارند، طوری که مدل‌سازی کیفیت رخساره‌های گازی با استفاده از تکنیک یادگیری عمیق با نظارت با دقت بیشتری (87%) نسبت به روش ناپارامتری پارزن (83%) انجام گرفته است. همچنین با اعمال الگوریتم‌های بهینه‌سازی دقت شبکه بیشتر شده است. بهترین دقت مربوط به شبکه LSTM بهینه شده با الگوریتم رقابت استعماری  (90%) است. گزارش‌ها و داده‌های ژئوشیمیایی مغزه‌های چاه، اعتبارسنجی بالای این مدل‌سازی‌ها را نشان می‌دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Introduction of Two Data-Driven Methods for Determining the Quality of Gas Facies in Western Australia

نویسندگان [English]

  • Y. Asgari Nezhad 1
  • A. Moradzadeh 2
1 Ph.D Student, Dept. of Mining Engineering, University of Tehran, Tehran, Iran
2 Professor, Dept. of Mining Engineering, University of Tehran, Tehran, Iran
چکیده [English]

In determining the optimal points of production drilling, it is important to identify areas of suitable reservoir quality. For this purpose, the use of geochemical data, which is usually small in number, is common. This data discontinuity creates information gaps. If one uses more continuous data so that its modeling accuracy is suitable, the drilling could be then performed with more success. In this study, seismic and well logs data were used to classify the quality of gas facies by two non-parametric statistical (Parzen) and supervised deep learning techniques (long-term short-term memory network (LSTM)). The LSTM network was then also optimized by two heuristic optimization methods (Imperialistic competition algorithm and Whale algorithm). The obtained results indicate that both methods produce good results in classification so that the modeling accuracy of gas facies quality using supervised deep learning technique (87%) is more than that of the non-parametric Parzen (83%) method. Moreover, the application of optimization algorithms has increased the classification accuracy. The best accuracy is related to the LSTM network optimized with the imperialistic competition algorithm (90%). Geochemical reports and well cores data show the high validity of these models.

کلیدواژه‌ها [English]

  • Quality of gas facies
  • LSTM network
  • Parzen
  • Imperialistic competition algorithm
  • Whale optimization algorithm

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سابقه مقاله

  • تاریخ دریافت: 11 اسفند 1399
  • تاریخ بازنگری: 02 تیر 1400
  • تاریخ پذیرش: 09 تیر 1400

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