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• W2998221813 abstract "Shear wave acoustic velocity (Vs) for the Bakken formation of the Williston Basin (North America) is predicted with high accuracy using an optimized data matching supervised learning algorithm; the transparent open box (TOB) learning network. The predictions are generated from a dataset reconstructed from a type well log data (seven petrophysical variables including Vs) and a discrete stratigraphic index (Str) distinguishing the main stratigraphic members. The dataset is divided into 1000 records equally spaced and arranged vertically and numbered sequentially (from 1–1000) from the top of the Bakken formation and extending into the underlying Three Forks formation. TOB predicts Vs for the whole of that dataset with its best-case solution achieving root mean squared error (RMSE) = 11.1 m/s, coefficient of determination (R2) = 0.9994). Data mining reveals that only eight data records exceed prediction errors of 70 m/s, with the maximum error of only 94 m/s. These few less accurate predictions are for data records located in the transition zones between the main stratigraphic members where Vs values change rapidly. Feature selection of alternative petrophysical variables to add or substitute in the TOB network have the potential to further reduce prediction errors in the formation transition zones. The TOB algorithm demonstrates its potential to be applied for more extensive and complex lithofacies and stratigraphic sequence modelling and to assess both continuous and discrete dependent variables." @default.
• W2998221813 created "2020-01-10" @default.
• W2998221813 creator A5028777364 @default.
• W2998221813 date "2020-02-01" @default.
• W2998221813 modified "2023-10-16" @default.
• W2998221813 title "Bakken stratigraphic and type well log learning network exploited to predict and data mine shear wave acoustic velocity" @default.
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• W2998221813 doi "https://doi.org/10.1016/j.jappgeo.2019.103936" @default.
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