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Supervised Machine Learning and Multiple Regression Approaches to Predict the Successfulness of Matrix Acidizing in Hydraulic Fractured Sandstone Formation.

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Title: Supervised Machine Learning and Multiple Regression Approaches to Predict the Successfulness of Matrix Acidizing in Hydraulic Fractured Sandstone Formation.
Authors: Kurniawan, Candra1,2, Azis, Muhammad Mufti1 muhammad.azis@ugm.ac.id, Ariyanto, Teguh1
Source: ASEAN Journal of Chemical Engineering. 2023, Vol. 23 Issue 1, following p113-127. 18p.
Database: Academic Search Ultimate
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  Data: Supervised Machine Learning and Multiple Regression Approaches to Predict the Successfulness of Matrix Acidizing in Hydraulic Fractured Sandstone Formation.
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  Data: <searchLink fieldCode="JN" term="%22ASEAN+Journal+of+Chemical+Engineering%22">ASEAN Journal of Chemical Engineering</searchLink>. 2023, Vol. 23 Issue 1, following p113-127. 18p.
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        Value: 10.22146/ajche.78255
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      – Code: eng
        Text: English
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      – TitleFull: Supervised Machine Learning and Multiple Regression Approaches to Predict the Successfulness of Matrix Acidizing in Hydraulic Fractured Sandstone Formation.
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            NameFull: Azis, Muhammad Mufti
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              Text: 2023
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