Research Article
Research on Microscopic Pore-throat Structure Characteristics Using Multiple Methods: A Case Study of the Chang 9 Reservoir in Ansai Oilfield
Wang Xixi*,
Meng Yue,
Li Yongfeng,
Hu Guilin
Issue:
Volume 13, Issue 2, April 2025
Pages:
16-24
Received:
7 March 2025
Accepted:
27 March 2025
Published:
31 March 2025
Abstract: This study presents a comprehensive investigation of the pore-throat characteristics in the Chang 9 tight sandstone reservoir of Ansai Oilfield, Ordos Basin, utilizing an integrated multiscale characterization approach. By combining image-based pore analysis, high-pressure and rate-controlled mercury intrusion porosimetry, nuclear magnetic resonance (NMR) relaxation spectroscopy, and nano-CT three-dimensional reconstruction, we established a complete pore-throat characterization methodology spanning multiple scales.The experimental results demonstrate that the reservoir exhibits a bimodal pore-throat distribution, with an average two-dimensional pore radius of 21.51 μm. The three-dimensional pore-throat network is characterized by predominant throat sizes of 0.15 μm (ranging 0.006-1 μm) and pore sizes of 120 μm (60-270 μm). NMR analysis reveals that the lower threshold radius for movable fluids ranges from 0.88 to 8.94 μm, showing significant positive correlation with reservoir quality parameters.A well-developed microfracture network with apertures of 0.07-4.80 μm was identified, which substantially enhances the reservoir's flow capacity. The successful integration of multiple characterization techniques enables complete pore-throat structure characterization across all relevant scales in tight sandstone reservoirs.These findings provide both theoretical foundations and practical guidance for sweet spot identification, reservoir evaluation, and hydrocarbon exploration/development in tight sandstone formations. The established methodology offers valuable insights for understanding fluid storage and flow mechanisms in low-permeability reservoirs.
Abstract: This study presents a comprehensive investigation of the pore-throat characteristics in the Chang 9 tight sandstone reservoir of Ansai Oilfield, Ordos Basin, utilizing an integrated multiscale characterization approach. By combining image-based pore analysis, high-pressure and rate-controlled mercury intrusion porosimetry, nuclear magnetic resonanc...
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Research Article
Construction of Depression Prediction Model Based on Machine Learning and Its Interpretability
Issue:
Volume 13, Issue 2, April 2025
Pages:
25-32
Received:
10 March 2025
Accepted:
8 April 2025
Published:
14 April 2025
Abstract: Objectives: The aim of this study was to construct depression prediction models based on machine learning algorithms, compared the performance of different machine learning models on depression risk prediction, and interpreted the model. Methods: A total of 2573 participants from the CHARLS database. LASSO and stepwise regression were used to screen for variables. The dataset is randomly divided into training set, validation set and test set according to 6:2:2. SMOTE resampling was used to balance the training set when fitted the model. Nine machine learning algorithms were used to construct the prediction model, inclpuding Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Elastic Network Regression (Enet), Support Vector Machine (SVM), Logistic Regression, Multilayer Perceptron (MLP), and K-Nearest Neighbor (KNN). The prediction ability of each machine learning classifier was evaluated on the test set according to the evaluation index, and the "optimal" model of this study was selected. Subsequently, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to analyze the interpretability of the optimal model. Results: The XGBoost model predicted the best performance among the 9 models. Its AUC value reached 0.908 and the clinical net benefit is the highest. The Delong test showed that there was a significant difference between the ROC curves of XGBoost and the other models (P<0.05). The global interpretation based on SHAP showed that life satisfaction, self-rated health status, sleep duration, and cognitive score were inversely proportional to the SHAP value. Female, rural residents, body aches and pains in any area, non-retirement, and limited Instrumental Activities of Daily Living (IADL) have a positive effect on depression. The local interpretation diagram based on SHAP and LIME showed the personalized risk prediction of a single sample. Conclusions: Machine learning models are an effectively tool for predict the risk of depression. The use of SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations can maximize the clinical advantages of machine learning, which is helpful to predict or detect patients at high risk of depression as early as possible, and to take comprehensive evaluation and early prevention and treatment of depression.
Abstract: Objectives: The aim of this study was to construct depression prediction models based on machine learning algorithms, compared the performance of different machine learning models on depression risk prediction, and interpreted the model. Methods: A total of 2573 participants from the CHARLS database. LASSO and stepwise regression were used to scree...
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,
Man Cui
