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

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

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: An intelligent approach for reservoir quality evaluation in tight sandstone reservoir using gradient boosting decision tree algorithm ; volume:14 ; number:1 ; year:2022 ; pages:629-645 ; extent:17 ; Open Geosciences ; 14, Heft 1 (2022), 629-645 (gesamt 17)

Contributors, Places and Time

• Creator: Ma, Longfei ; Xiao, Hanmin ; Tao, Jingwei ; Zheng, Taiyi ; Zhang, Haiqin

Further information

• DOI: 10.1515/geo-2022-0354
• URN: urn:nbn:de:101:1-2022071914053605549666
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:24 AM CEST