Carbonate lithofacies identification using an improved light gradient boosting machine and conventional logs: a demonstration using pre-salt lacustrine reservoirs, Santos Basin

Gu, Yufeng; Zhang, Daoyong; Bao, Zhidong

doi:10.1007/s13146-021-00746-1

Cited by 4 publications

(2 citation statements)

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“…It has a small memory footprint, efficient training speed, accurate prediction ability, can handle large-scale data and high-dimensional features, and provides flexible parameter adjustment and parallelization functions, which can effectively improve model performance and efficiency. 19,20 Shale gas has been a hot topic in recent years; however, few scholars have researched the automatic recognition of shale lithofacies. Therefore, more in-depth studies are required.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a new algorithm, LightGBM, was developed based on XGBoost. It has a small memory footprint, efficient training speed, accurate prediction ability, can handle large‐scale data and high‐dimensional features, and provides flexible parameter adjustment and parallelization functions, which can effectively improve model performance and efficiency 19,20 …”

Section: Introductionmentioning

confidence: 99%

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Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Liu,

Zhu,

Zhai

et al. 2023

Energy Science & Engineering

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Lithofacies form the basis for evaluating shale gas fields and play an important role in gas reservoir enrichment. The accurate identification of shale lithofacies is key for exploration and development. Based on well‐logged data, the accuracy of mineral content prediction using machine‐learning regression models is not ideal. Therefore, feature derivation was introduced to enhance the correlation between minerals and lithofacies and improve the data expression ability. Four machine‐learning models for mineral regression were established based on feature‐derived data sets: LightGBM, XGBoost, artificial neural network, and support vector machine. By calculating the evaluation metrics of each model, we found that LightGBM had the best prediction performance. To compare and confirm the accuracy of the model in identifying lithofacies, this study established a new method, MT‐LightGBM, which combines the LightGBM mineral content regression model with mineral ternary diagrams to identify lithofacies. By using the MT‐LightGBM model and LightGBM classification models to identify the target lithofacies, it was found that the accuracy of lithofacies identification of MT‐LightGBM reached 94%. This accuracy is high and is of great significance for understanding and evaluating underground shale reservoirs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%