Prediction Method for CT Pore Extraction Thresholds in Tight Sandstone Based on NMR Porosity Constraint and Grayscale Statistical Features
Abstract
Three-dimensional core CT scanning technology is widely used in the characterization of reservoir microstructure. However, traditional porosity extraction methods rely on subjectively selected grayscale segmentation thresholds, leading to significant uncertainties in experimental analysis results. This study proposes a method for predicting the porosity extraction threshold of core CT data. First, the porosity of the core is calibrated using nuclear magnetic resonance (NMR) scanning. Then, the grayscale distribution characteristics of the core CT data are calculated, including the mean, median, mode, standard deviation, and kurtosis. The correlation between the porosity extraction threshold and these grayscale distribution characteristics is analyzed. Finally, a ridge regression model is used to establish a method for predicting the porosity extraction threshold. This study conducted NMR and three-dimensional CT scanning experiments on 11 tight sandstone cores. The experiments revealed a strong positive correlation between the mean, median, and mode of the grayscale distribution characteristics of the three-dimensional core CT data and the porosity extraction threshold, with correlation coefficients greater than 0.94. The correlation coefficient between kurtosis and the porosity extraction threshold was 0.82. The porosity extraction threshold prediction method established by combining these five gray-scale distribution characteristics exhibits high accuracy, with a training set determination coefficient R^2 = 0.984 and a mean absolute percentage error (MAPE) of 0.69%. Furthermore, this porosity extraction threshold prediction method demonstrates strong generalization ability, with a cross-validation determination coefficient CV-R2 of 0.858. Experimental results show that the proposed method can accurately predict the porosity extraction threshold of 3D core CT data, avoiding the subjective risks of manual threshold selection and providing support for the application of digital core technology in tight sandstone.
Article Type: Research Article
Cited as:
Wang X, Zhang G, Zhao D. 2026. Prediction Method for CT Pore Extraction Thresholds in Tight Sandstone Based on NMR Porosity Constraint and Grayscale Statistical Features. GeoStorage, 2(3), 216-229.
DOI:
https://doi.org/10.46690/gs.2026.03.02Keywords:
Tight sandstone, digital core, grayscale statistics, pore extraction thresholdReferences
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