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A semi-automatically identifying lithofacies approach from wireline logs using fuzzy clustering

Authors: Duy Thong Kieu; Quang Man Ha; Viet Dung Bui; Huy Hien Doan; Nguyen Binh Kieu;

A semi-automatically identifying lithofacies approach from wireline logs using fuzzy clustering

Abstract

Lithofacies is important for reservoir evaluation. In this work, we present a workflow to define the lithofacies from wireline logs. The workflow includes three phases: in the first phase the boundaries are automatically defined from wireline logs by using the recurrence technique; in the second phase, we extract the data set from wireline logs within the boundaries and put it in a modified fuzzy c-means clustering process. The second phase results are analysed to identify the facies. Noting that our workflow can be automated if we have an available dataset including labels to build a prediction model in the third phase. We apply our workflow to a data set in Nam Con Son basin, Vietnam. The results are comparable with core data.

Open-Access Online Publication: May 29, 2023

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Keywords

lithofacies, fuzzy c-means, Nam Con Son Basin., supervised learning

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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