The Application of Pattern Recognition in Electrofacies Analysis

Li, Huan; Yang, Xiao; Wei, Wenhong

doi:10.1155/2014/640406

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…It also represents the target of electrofacies classification and the way in where the final categorization will be interpreted and used. In our experiment, we selected five electrofacies classes to be constructed by a non hierarchical k-means clustering algorithm, which is one of the most popular and widespread partitioning clustering algorithms because of its superior feasibility and efficiency in dealing with a large amount of data [39]. Several studies have suggested and used k-means clustering for constructing electrofacies [18,27].…”

Section: Clustering the Logsmentioning

confidence: 99%

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

et al. 2020

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Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the crucial requirements in petroleum engineering is to interpret a bed’s lithology, which can be done by grouping a formation into electrofacies. In the past, geophysical modelling, petro-physical analysis, artificial intelligence and several statistical method approaches have been implemented to interpret lithology. In this research, important well log features are selected by using the Extra Tree Classifier (ETC), and then five individual electrofacies are constructed by using the selected well log features. Finally, a rough set theory (RST)-based whitebox classification approach is proposed to classify the electrofacies by generating decision rules. These rules are later on used to determine the lithology classes and we found that RST is beneficial for performing data mining tasks such as data classification and rule extraction from uncertain and vague well log datasets. A comparison study is also provided, where we use support vector machine (SVM), deep learning based on feedforward multilayer perceptron (MLP) and random forest classifier (RFC) to compare the electrofacies classification accuracy.

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Section: Clustering the Logsmentioning

confidence: 99%

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

et al. 2020

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K-Ortalamalar Kümelenme Yöntemi İçin Çift K Başlatma Algoritması

Yücelen¹,

Baykal²

2021

European Journal of Science and Technology

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ÖzVeri madenciliğinin en dikkat çekici konularından biri olan kümeleme yöntemleri, bu alanın en yoğun araştırma sahası olup kümeleme üzerine bir çok teknik ve bağlı yöntemler bulunmaktadır.Bu alandaki çalışmaların bir kısmı daha önce mevcut olan algoritmaların güncellenmesiyle elde edilmiş ve performansları değerlendirilmiştir.Kümelemenin en çok ilgi duyulan konusu K-Ortalamalar yöntemidir.K-Ortalamalar algoritması her çalıştırıldığında, başlangıç merkezlerinin rastgele seçilmesi nedeniyle farklı küme çıktıları döndürür.Bu nedenle, sonuçların güvenilirliği olumsuz etkilenir ve kümeleme doğruluğu için yineleme sayısı artar.Bu yöntemin doğruluğunu arttırmaya çalışan yöntemlerden biri de K-Ortalamalar++ yöntemidir. Bu çalışmada K-Ortalamalar algoritmasının başarısının arttırılması hedeflenmiştir. Sentetik veri kümesine çift k olarak adlandırdığımız önerilen yöntem uygulanmıştır.Çift k yönteminin, nihai kümeleme etiketlerini bulmada K-Ortalamalar ve K-Ortalamalar++ yöntemine göre daha başarılı olduğu gözlenmiştir.

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The Application of Pattern Recognition in Electrofacies Analysis

Cited by 2 publications

References 9 publications

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

K-Ortalamalar Kümelenme Yöntemi İçin Çift K Başlatma Algoritması

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