A new NMR-data-based method for predicting petrophysical properties of tight sandstone reservoirs
Evaluating the permeability and irreducible water saturation of tight sandstone reservoirs is challenging. This study uses distribution functions to fit measured NMR T2 distributions of tight sandstone reservoirs and extract parameters for characterizing pore size distribution. These parameters are...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-04-01
|
| Series: | Energy Geoscience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666759221000688 |
| _version_ | 1827981856654491648 |
|---|---|
| author | Mi Liu Ranhong Xie Jun Li Hao Li Song Hu Youlong Zou |
| author_facet | Mi Liu Ranhong Xie Jun Li Hao Li Song Hu Youlong Zou |
| author_sort | Mi Liu |
| collection | DOAJ |
| description | Evaluating the permeability and irreducible water saturation of tight sandstone reservoirs is challenging. This study uses distribution functions to fit measured NMR T2 distributions of tight sandstone reservoirs and extract parameters for characterizing pore size distribution. These parameters are then used to establish prediction models for permeability and irreducible water saturation of reservoirs. Results of comparing the fit of the T2 distributions by the Gauss and Weibull distribution functions show that the fitting accuracy with the Weibull distribution function is higher. The physical meaning of the statistical parameters of the Weibull distribution function is defined to establish nonlinear prediction models of permeability and irreducible water saturation using the radial basis function (RBF) method. Correlation coefficients between the predicted values by the established models and the measured values of the tight sandstone core samples are 0.944 for permeability and 0.851 for irreducible water saturation, which highlight the effectiveness of the prediction models. |
| first_indexed | 2024-04-09T22:15:42Z |
| format | Article |
| id | doaj.art-f4e795c98e004d3a80bad85288ca4452 |
| institution | Directory Open Access Journal |
| issn | 2666-7592 |
| language | English |
| last_indexed | 2024-04-09T22:15:42Z |
| publishDate | 2023-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Energy Geoscience |
| spelling | doaj.art-f4e795c98e004d3a80bad85288ca44522023-03-23T04:37:13ZengKeAi Communications Co., Ltd.Energy Geoscience2666-75922023-04-0142100083A new NMR-data-based method for predicting petrophysical properties of tight sandstone reservoirsMi Liu0Ranhong Xie1Jun Li2Hao Li3Song Hu4Youlong Zou5Petroleum Exploration and Production Research Institute, SINOPEC, Beijing, 100083, ChinaState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, 102249, China; Corresponding author.Petroleum Exploration and Production Research Institute, SINOPEC, Beijing, 100083, ChinaPetroleum Exploration and Production Research Institute, SINOPEC, Beijing, 100083, ChinaPetroleum Exploration and Production Research Institute, SINOPEC, Beijing, 100083, ChinaPetroleum Exploration and Production Research Institute, SINOPEC, Beijing, 100083, ChinaEvaluating the permeability and irreducible water saturation of tight sandstone reservoirs is challenging. This study uses distribution functions to fit measured NMR T2 distributions of tight sandstone reservoirs and extract parameters for characterizing pore size distribution. These parameters are then used to establish prediction models for permeability and irreducible water saturation of reservoirs. Results of comparing the fit of the T2 distributions by the Gauss and Weibull distribution functions show that the fitting accuracy with the Weibull distribution function is higher. The physical meaning of the statistical parameters of the Weibull distribution function is defined to establish nonlinear prediction models of permeability and irreducible water saturation using the radial basis function (RBF) method. Correlation coefficients between the predicted values by the established models and the measured values of the tight sandstone core samples are 0.944 for permeability and 0.851 for irreducible water saturation, which highlight the effectiveness of the prediction models.http://www.sciencedirect.com/science/article/pii/S2666759221000688NMRPermeabilityIrreducible water saturationTight sandstoneWeibull distribution functionRBF |
| spellingShingle | Mi Liu Ranhong Xie Jun Li Hao Li Song Hu Youlong Zou A new NMR-data-based method for predicting petrophysical properties of tight sandstone reservoirs Energy Geoscience NMR Permeability Irreducible water saturation Tight sandstone Weibull distribution function RBF |
| title | A new NMR-data-based method for predicting petrophysical properties of tight sandstone reservoirs |
| title_full | A new NMR-data-based method for predicting petrophysical properties of tight sandstone reservoirs |
| title_fullStr | A new NMR-data-based method for predicting petrophysical properties of tight sandstone reservoirs |
| title_full_unstemmed | A new NMR-data-based method for predicting petrophysical properties of tight sandstone reservoirs |
| title_short | A new NMR-data-based method for predicting petrophysical properties of tight sandstone reservoirs |
| title_sort | new nmr data based method for predicting petrophysical properties of tight sandstone reservoirs |
| topic | NMR Permeability Irreducible water saturation Tight sandstone Weibull distribution function RBF |
| url | http://www.sciencedirect.com/science/article/pii/S2666759221000688 |
| work_keys_str_mv | AT miliu anewnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT ranhongxie anewnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT junli anewnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT haoli anewnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT songhu anewnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT youlongzou anewnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT miliu newnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT ranhongxie newnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT junli newnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT haoli newnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT songhu newnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs AT youlongzou newnmrdatabasedmethodforpredictingpetrophysicalpropertiesoftightsandstonereservoirs |