Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods
With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical hete...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2020/3713525 |
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| _version_ | 1832566102117318656 |
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| author | Hongqing Song Shuyi Du Ruifei Wang Jiulong Wang Yuhe Wang Chenji Wei Qipeng Liu |
| author_facet | Hongqing Song Shuyi Du Ruifei Wang Jiulong Wang Yuhe Wang Chenji Wei Qipeng Liu |
| author_sort | Hongqing Song |
| collection | DOAJ |
| description | With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods. |
| format | Article |
| id | doaj-art-e6d5463702f14a408a56a8742b5975e9 |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-e6d5463702f14a408a56a8742b5975e92025-02-03T01:05:13ZengWileyGeofluids1468-81151468-81232020-01-01202010.1155/2020/37135253713525Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning MethodsHongqing Song0Shuyi Du1Ruifei Wang2Jiulong Wang3Yuhe Wang4Chenji Wei5Qipeng Liu6School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266555, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaStrategic Research Center of Oil and Gas Resources, Ministry of Natural Resources of PRC, Beijing 100812, ChinaWith the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods.http://dx.doi.org/10.1155/2020/3713525 |
| spellingShingle | Hongqing Song Shuyi Du Ruifei Wang Jiulong Wang Yuhe Wang Chenji Wei Qipeng Liu Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods Geofluids |
| title | Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods |
| title_full | Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods |
| title_fullStr | Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods |
| title_full_unstemmed | Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods |
| title_short | Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods |
| title_sort | potential for vertical heterogeneity prediction in reservoir basing on machine learning methods |
| url | http://dx.doi.org/10.1155/2020/3713525 |
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