Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units
TLDR
In this paper , the authors used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran.Abstract:
Abstract Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. The relationship between porosity and permeability improved using hydraulic flow unit techniques significantly. In this study, the correlation coefficient between porosity and permeability improves and increases in each hydraulic flow unit using the flow zone index method. So that in the general case, all samples increased from 0.55 to 0.81 in the first hydraulic flow unit and finally 0.94 in the fourth hydraulic flow unit. The samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units. read more
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Journal ArticleDOI
The electrical resistivity log as an aid in determining some reservoir characteristics
TL;DR: The usefulness of the electrical resistivity log in determining reservoir characteristics is governed largely by: (1) the accuracy with which the true resistivity of the formation can be determined; (2) the scope of detailed data concerning the relation of resistivity measurements to formation characteristics; (3) the available information concerning the conductivity of connate or formation waters; and (4) the extent of geologic knowledge regarding probable changes in facies within given horizons, both vertically and laterally, particularly in relation to the resultant effect on the electrical properties of the reservoir as mentioned in this paper.
Proceedings ArticleDOI
Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic (Flow) Units and Predict Permeability in Uncored Intervals/Wells
TL;DR: In this article, a new, practical and theoretically correct methodology is proposed for identi$cation and characterization of hydraulic units based on a modified Kozeny-Carmen equation and the concept of mean hydraulic raditis.
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Pore system characterisation in heterogeneous carbonates: An alternative approach to widely-used rock-typing methodologies
TL;DR: In this paper, the authors used a giant carbonate reservoir in Northern Oman and defined rock types on the basis of pore geometry, whilst retaining distinct, geological descriptors, to ensure that each rock type could be defined based on both its petrophysical properties and behaviour during hydrocarbon recovery.
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A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field
TL;DR: In this article, a fuzzy c-means clustering technique was used for rock type classification using porosity and permeability data, based on the fuzzy possibility concept, an algorithm was prepared to estimate clustering derived rock types from well log data.
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Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA
TL;DR: In this paper, the authors compared various data-driven supervised and unsupervised computational approaches, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Self-Organizing Map (SOM), and Multi-Resolution Graph-based Clustering (MRGC), to quantify the relationship between geochemical and core data.