Applying deep learning method to develop a fracture modeling for a fractured carbonate reservoir using geologic, seismic and petrophysical data
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 57
فایل این مقاله در 11 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJMGE-57-3_010
تاریخ نمایه سازی: 2 آبان 1402
چکیده مقاله:
Fractures are one of the most important geological features that affect production from most carbonate reservoirs. A large amount of the world’s hydrocarbon resources are located in fractured reservoirs and the identification of fractures is one of the important steps in reservoir development. Due to the high cost of tools that are used in the petroleum industry to identify fractures such as image logs, and their inaccessibility in most of the studied areas, it is often tried to use other available data to identify fractures. Due to the ever-increasing progress of data-driven methods such as neural networks and machine learning, this study has tried to apply a ۱D-Convolutional Neural Network (۱D-CNN) which is one of the deep learning algorithms on well-logging data and seismic attributes in a carbonate reservoir to identify the existing fractures in the investigating area. The approach used in this research is a binary classification which is applied first in the well location. To validate the method, results are compared with the reports obtained from image logs. Finally, the fracture density map is drawn in the entire reservoir area.
کلیدواژه ها:
نویسندگان
Fateme Heydarpour
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Abbas Bahroudi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :