ArticleScientific reports2025
Porosity prediction of tight reservoir rock using well logging data and machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Dual-attention residual U-Net with Huber loss for robust and efficient porosity prediction from well logs.Scientific reports · 2026Article
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9 authors.
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Abstract
The accurate quantification of porosity in tight reservoirs is crucial for optimizing oil and gas exploration and production. Traditional predictive models often face challenges such as high costs, low efficiency, and limited accuracy, hindering effective exploration activities. To address these issues, we apply advanced machine learning algorithms-gradient boosting decision tree (GBDT), random forest, XGBoost, and multilayer perceptron-using well logging data, including acoustic time (AC), well logging (CAL), compensating neutrons (CNL), density (DEN), natural gamma (GR), resistivity (RT), and spontaneous potential (SP). These models are further optimized with the particle swarm optimization (PSO) algorithm to enhance their predictive accuracy. Comparative analysis reveals that the PSO-GBDT model outperforms other models, achieving an R
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