Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning
Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance lo...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/6/1458 |
| _version_ | 1827306399233736704 |
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| author | Jianpeng Zhao Qi Wang Wei Rong Jingbo Zeng Yawen Ren Hui Chen |
| author_facet | Jianpeng Zhao Qi Wang Wei Rong Jingbo Zeng Yawen Ren Hui Chen |
| author_sort | Jianpeng Zhao |
| collection | DOAJ |
| description | Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models. |
| first_indexed | 2024-04-24T18:20:33Z |
| format | Article |
| id | doaj.art-af09331998954553a110f0d43c858df6 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-24T18:20:33Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj.art-af09331998954553a110f0d43c858df62024-03-27T13:35:48ZengMDPI AGEnergies1996-10732024-03-01176145810.3390/en17061458Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine LearningJianpeng Zhao0Qi Wang1Wei Rong2Jingbo Zeng3Yawen Ren4Hui Chen5School of Earth Sciences & Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaSchool of Earth Sciences & Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaGeological Research Institute, China Petroleum Logging Co., Ltd., Xi’an 710075, ChinaGeological Research Institute, China Petroleum Logging Co., Ltd., Xi’an 710075, ChinaGeological Research Institute, China Petroleum Logging Co., Ltd., Xi’an 710075, ChinaGeological Research Institute, China Petroleum Logging Co., Ltd., Xi’an 710075, ChinaReservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models.https://www.mdpi.com/1996-1073/17/6/1458machine learningpermeability predictioncarbonate reservoirNMR loggingXGBoost method |
| spellingShingle | Jianpeng Zhao Qi Wang Wei Rong Jingbo Zeng Yawen Ren Hui Chen Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning Energies machine learning permeability prediction carbonate reservoir NMR logging XGBoost method |
| title | Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning |
| title_full | Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning |
| title_fullStr | Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning |
| title_full_unstemmed | Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning |
| title_short | Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning |
| title_sort | permeability prediction of carbonate reservoir based on nuclear magnetic resonance nmr logging and machine learning |
| topic | machine learning permeability prediction carbonate reservoir NMR logging XGBoost method |
| url | https://www.mdpi.com/1996-1073/17/6/1458 |
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