Reservoir Parameter Prediction Based on the Neural Random Forest Model
Porosity and saturation are the basis for describing reservoir properties and formation characteristics. The traditional, empirical, and formulaic methods are unable to accurately capture the nonlinear mapping relationship between log data and reservoir physical parameters. To solve this problem, in...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-05-01
|
| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.888933/full |
| _version_ | 1811234481365843968 |
|---|---|
| author | Mingchuan Wang Dongjun Feng Donghui Li Jiwei Wang |
| author_facet | Mingchuan Wang Dongjun Feng Donghui Li Jiwei Wang |
| author_sort | Mingchuan Wang |
| collection | DOAJ |
| description | Porosity and saturation are the basis for describing reservoir properties and formation characteristics. The traditional, empirical, and formulaic methods are unable to accurately capture the nonlinear mapping relationship between log data and reservoir physical parameters. To solve this problem, in this study, a novel hybrid model (NRF) combining neural network (NN) and random forest (RF) was proposed based on well logging data to predict the porosity and saturation of shale gas reservoirs. The database includes six horizontal wells, and the input logs include borehole diameter, neutron, density, gamma-ray, and acoustic and deep investigate double lateral resistivity log. The porosity and saturation were chosen as outputs. The NRF model with independent and joint training was designed to extract key features from well log data and physical parameters. It provides a promising method for forecasting the porosity and saturation with R2 above 0.94 and 0.82 separately. Compared with baseline models (NN and RF), the NRF model with joint training obtains the unsurpassed performance to predict porosity with R2 above 0.95, which is 1.1% higher than that of the NRF model with independent training, 3.9% higher than RF, and superiorly greater than NN. For the prediction of saturation, the NRF model with joint training is still superior to other algorithms, with R2 above 0.84, which is 2.1% higher than that of the NRF model with independent training and 7.0% higher than RF. Furthermore, the NRF model has a similar data distribution with measured porosity and saturation, which demonstrates the NRF model can achieve greater stability. It was proven that the proposed NRF model can capture the complex relationship between the logging data and physical parameters more accurately, and can serve as an economical and reliable alternative tool to give a reliable prediction. |
| first_indexed | 2024-04-12T11:37:01Z |
| format | Article |
| id | doaj.art-effdc26f7fab406c8a743364bba71631 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-12T11:37:01Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-effdc26f7fab406c8a743364bba716312022-12-22T03:34:49ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-05-011010.3389/feart.2022.888933888933Reservoir Parameter Prediction Based on the Neural Random Forest ModelMingchuan WangDongjun FengDonghui LiJiwei WangPorosity and saturation are the basis for describing reservoir properties and formation characteristics. The traditional, empirical, and formulaic methods are unable to accurately capture the nonlinear mapping relationship between log data and reservoir physical parameters. To solve this problem, in this study, a novel hybrid model (NRF) combining neural network (NN) and random forest (RF) was proposed based on well logging data to predict the porosity and saturation of shale gas reservoirs. The database includes six horizontal wells, and the input logs include borehole diameter, neutron, density, gamma-ray, and acoustic and deep investigate double lateral resistivity log. The porosity and saturation were chosen as outputs. The NRF model with independent and joint training was designed to extract key features from well log data and physical parameters. It provides a promising method for forecasting the porosity and saturation with R2 above 0.94 and 0.82 separately. Compared with baseline models (NN and RF), the NRF model with joint training obtains the unsurpassed performance to predict porosity with R2 above 0.95, which is 1.1% higher than that of the NRF model with independent training, 3.9% higher than RF, and superiorly greater than NN. For the prediction of saturation, the NRF model with joint training is still superior to other algorithms, with R2 above 0.84, which is 2.1% higher than that of the NRF model with independent training and 7.0% higher than RF. Furthermore, the NRF model has a similar data distribution with measured porosity and saturation, which demonstrates the NRF model can achieve greater stability. It was proven that the proposed NRF model can capture the complex relationship between the logging data and physical parameters more accurately, and can serve as an economical and reliable alternative tool to give a reliable prediction.https://www.frontiersin.org/articles/10.3389/feart.2022.888933/fulllogging interpretationmachine learningreservoir parameter estimationneural random forestwell logs |
| spellingShingle | Mingchuan Wang Dongjun Feng Donghui Li Jiwei Wang Reservoir Parameter Prediction Based on the Neural Random Forest Model Frontiers in Earth Science logging interpretation machine learning reservoir parameter estimation neural random forest well logs |
| title | Reservoir Parameter Prediction Based on the Neural Random Forest Model |
| title_full | Reservoir Parameter Prediction Based on the Neural Random Forest Model |
| title_fullStr | Reservoir Parameter Prediction Based on the Neural Random Forest Model |
| title_full_unstemmed | Reservoir Parameter Prediction Based on the Neural Random Forest Model |
| title_short | Reservoir Parameter Prediction Based on the Neural Random Forest Model |
| title_sort | reservoir parameter prediction based on the neural random forest model |
| topic | logging interpretation machine learning reservoir parameter estimation neural random forest well logs |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.888933/full |
| work_keys_str_mv | AT mingchuanwang reservoirparameterpredictionbasedontheneuralrandomforestmodel AT dongjunfeng reservoirparameterpredictionbasedontheneuralrandomforestmodel AT donghuili reservoirparameterpredictionbasedontheneuralrandomforestmodel AT jiweiwang reservoirparameterpredictionbasedontheneuralrandomforestmodel |