An intelligent approach for reservoir quality evaluation in tight sandstone reservoir using gradient boosting decision tree algorithm

نویسندگان

چکیده

Abstract This article focuses on the study of identifying quality tight sandstone reservoirs based machine learning. The learning method – Gradient Boosting Decision Tree (GBDT) algorithm is used to design and classify reservoir quality. First, it logging data, core observation, cast thin section, physical statistics. permeability, porosity, resistivity, mud content, sand-to-ground ratio, sand thickness were preferred as evaluation criteria in area, gray correlation was obtain categories construct training datasets. GBDT train test obtained dataset. It found that recognition accuracy model 95% by confusion matrix analysis. In addition, compared with four commonly prediction methods (Bayesian discriminant method, random forest, support vector machine, artificial neural network) for verifying reliability model. Finally, identify well verified production data. research results show can become an important tool rapid real-time evaluation.

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ژورنال

عنوان ژورنال: Open Geosciences

سال: 2022

ISSN: ['2391-5447']

DOI: https://doi.org/10.1515/geo-2022-0354