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• W4296311706 endingPage "104814" @default.
• W4296311706 startingPage "104814" @default.
• W4296311706 abstract "Correlation of the petrophysical attributes of the cored intervals with relevant well log data being available for the majority of drilled wells allows a rapid assessment of the poro-perm properties of the reservoir rocks and generates a continuous record in uncored intervals while minimizing sampling concerns. Therefore, the ideal approach to assess the heterogeneity of the reservoir would utilize the machine learning methods to correlate the petrophysical features with well log data. As a result, in this study, an HFU derivation procedure was implemented to well log data from two wells in a mixed carbonate-siliciclastic formation. This procedure includes (a) extraction of HFUs from core poro-perm data using Amaefule's proposed hydraulic flow units, (b) Jointly use of the find change and the silhouette to multiple class membership detection of the HFU content, (c) sedimentological analysis of main processes affecting the reservoir quality (d) well log data conversion to HFUs using the BG and FIS models. In this study, classification rules are developed to directly estimating the hydraulic flow units' (HFU) memberships from well log data using bagging (BG) and Fuzzy Inference System (FIS) algorithms. The approach is proved to be efficient in explicitly deriving HFUs from the well log data after conceptualization of classification rules. Quantitative comparisons of the results from BG and FIS shows that BG is readily superior to FIS in terms of classification accuracy. Moreover, it is shown that BG accuracy enhances with increasing number of weak classifiers, as a controlling parameter." @default.
• W4296311706 created "2022-09-20" @default.
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• W4296311706 date "2022-11-01" @default.
• W4296311706 modified "2023-09-24" @default.
• W4296311706 title "Intelligent and statistical analysis to estimating the hydraulic flow units: A case study from the Kupal oilfield, SW Iran" @default.
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• W4296311706 doi "https://doi.org/10.1016/j.jappgeo.2022.104814" @default.
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