An integration of conventional rock typing methods and fuzzy Cmeans clustering algorithm: a case study
سال انتشار: 1400
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 186
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شناسه ملی سند علمی:
NTOGP03_032
تاریخ نمایه سازی: 3 تیر 1401
چکیده مقاله:
Determination of rock types in hydrocarbon reservoirs (whether carbonate or clastic) is veryimportant. Therefore, various methods for identifying rock types have been introduced and developedin recent years. In this study, we have applied some conventional rock typing methods in combinationwith a clustering machine learning algorithm in order to optimize the number of clusters and eventuallygroup input data in accurate rock types. The results show that flow zone indicator (FZI) methodintegrated with the fuzzy C-means (FCM) algorithm was the best approach for detecting rock types withthe range of correlation coefficient (R۲) from ۰.۸۱ to ۰.۸۷.
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نویسندگان
Mohammad Hosein Khosravi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Mahdi Kheirollahi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran