Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network
Pore-throat radius is one of the key parameters that characterizes the microscopic pore structure of rock, which has an important impact on oil-gas seepage and the prediction of remaining oil’s microscopic distribution. Currently, the quantitative characterization of a pore-throat radius mainly reli...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/21/7277 |
| _version_ | 1797631990656139264 |
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| author | Yao Hong Shunming Li Hongliang Wang Pengcheng Liu Yuan Cao |
| author_facet | Yao Hong Shunming Li Hongliang Wang Pengcheng Liu Yuan Cao |
| author_sort | Yao Hong |
| collection | DOAJ |
| description | Pore-throat radius is one of the key parameters that characterizes the microscopic pore structure of rock, which has an important impact on oil-gas seepage and the prediction of remaining oil’s microscopic distribution. Currently, the quantitative characterization of a pore-throat radius mainly relies on rock-core experiments, then uses capillary pressure functions, e.g., the J-function, to predict the pore-throat radius of rocks which have not undergone core experiments. However, the prediction accuracy of the J-function struggles to meet the requirements of oil field development during a high water-cut stage. To solve this issue, in this study, based on core experimental data, we established a deep neural network (DNN) model to predict the maximum pore-throat radius R<sub>max</sub>, median pore-throat radius R<sub>50</sub>, and minimum flow pore-throat radius R<sub>min</sub> of rocks for the first time. To improve the prediction accuracy of the pore-throat radius, the key components of the DNN are preferably selected and the hyperparameters are adjusted, respectively. To illustrate the effectiveness of the DNN model, core samples from Q Oilfield were selected as the case study. The results show that the evaluation metrics of the DNN notably outperform when compared to other mature machine learning methods and conventional J-function method; the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are decreased by 14–57.8%, 32.4–64.3% and 13.5–48.9%, respectively, and the predicted values are closer to the true values of the pore-throat radius. This method provides a new perspective on predicting the pore-throat radius of rocks, and it is of great significance for predicting the dominant waterflow pathway and in-depth profile control optimization. |
| first_indexed | 2024-03-11T11:30:34Z |
| format | Article |
| id | doaj.art-3f887cd5812f487496b2c1ab802b5892 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T11:30:34Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3f887cd5812f487496b2c1ab802b58922023-11-10T15:02:02ZengMDPI AGEnergies1996-10732023-10-011621727710.3390/en16217277Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural NetworkYao Hong0Shunming Li1Hongliang Wang2Pengcheng Liu3Yuan Cao4School of Energy Resources, China University of Geosciences, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaSchool of Energy Resources, China University of Geosciences, Beijing 100083, ChinaSchool of Energy Resources, China University of Geosciences, Beijing 100083, ChinaShanxi Coalbed Methane Branch of Huabei Oilfield Company, PetroChina, Jincheng 048000, ChinaPore-throat radius is one of the key parameters that characterizes the microscopic pore structure of rock, which has an important impact on oil-gas seepage and the prediction of remaining oil’s microscopic distribution. Currently, the quantitative characterization of a pore-throat radius mainly relies on rock-core experiments, then uses capillary pressure functions, e.g., the J-function, to predict the pore-throat radius of rocks which have not undergone core experiments. However, the prediction accuracy of the J-function struggles to meet the requirements of oil field development during a high water-cut stage. To solve this issue, in this study, based on core experimental data, we established a deep neural network (DNN) model to predict the maximum pore-throat radius R<sub>max</sub>, median pore-throat radius R<sub>50</sub>, and minimum flow pore-throat radius R<sub>min</sub> of rocks for the first time. To improve the prediction accuracy of the pore-throat radius, the key components of the DNN are preferably selected and the hyperparameters are adjusted, respectively. To illustrate the effectiveness of the DNN model, core samples from Q Oilfield were selected as the case study. The results show that the evaluation metrics of the DNN notably outperform when compared to other mature machine learning methods and conventional J-function method; the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are decreased by 14–57.8%, 32.4–64.3% and 13.5–48.9%, respectively, and the predicted values are closer to the true values of the pore-throat radius. This method provides a new perspective on predicting the pore-throat radius of rocks, and it is of great significance for predicting the dominant waterflow pathway and in-depth profile control optimization.https://www.mdpi.com/1996-1073/16/21/7277pore-throat radiusdeep neural networkhyperparameter optimizationJ-functionquantitative characterization |
| spellingShingle | Yao Hong Shunming Li Hongliang Wang Pengcheng Liu Yuan Cao Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network Energies pore-throat radius deep neural network hyperparameter optimization J-function quantitative characterization |
| title | Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network |
| title_full | Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network |
| title_fullStr | Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network |
| title_full_unstemmed | Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network |
| title_short | Quantitative Prediction of Rock Pore-Throat Radius Based on Deep Neural Network |
| title_sort | quantitative prediction of rock pore throat radius based on deep neural network |
| topic | pore-throat radius deep neural network hyperparameter optimization J-function quantitative characterization |
| url | https://www.mdpi.com/1996-1073/16/21/7277 |
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