کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6481600 1399419 2016 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Two intelligent pattern recognition models for automatic identification of textural and pore space characteristics of the carbonate reservoir rocks using thin section images
ترجمه فارسی عنوان
دو مدل تشخیص الگو هوشمند برای شناسایی خودکار ویژگی های فواره بافت و فضای حفره های مخزن کربنات با استفاده از تصاویر بخش نازک
کلمات کلیدی
ویژگی های متنوع، طبقه بندی دانماه، تجزیه و تحلیل زبری، شناسایی فضای پوسته، سنگ های کربناته، الگوهای تشخیص الگو،
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علوم زمین و سیاره ای (عمومی)
چکیده انگلیسی


- Image analysis and pattern recognition are two potential techniques for reservoir zonation.
- The gray scale images are used for textural characterization.
- The black and white images are employed for pore space identification.
- The model identifies four main classes of Dunham classification for carbonate rocks.

Over the last two decades pattern recognition approaches have attracted engineers to solve real world problems more accurately through the development of computational technology. In the present research, the capabilities of intelligent systems are employed to develop two algorithms for identification of textural and pore space characteristics of carbonate rocks from thin section images. The texture identifier model classifies the images based on Dunham classification, while the porosity analyzer model determines the percentage of each type of pore spaces in the image. The texture identifier model extracts thirteen features to recognize texture type and the porosity analyzer determines percentage of each type of porosity based on eleven features extracting from the thin section image. Finally, two confusion matrixes are used to evaluate the performance of the developed models. The results show that the models perform reliably from the perspective of petroleum geology for studying carbonate reservoir rocks.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Natural Gas Science and Engineering - Volume 35, Part A, September 2016, Pages 944-955
نویسندگان
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