Research on neural network prediction method for upgrading scale of natural gas reserves
With the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1253495/full |
| _version_ | 1797346581530279936 |
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| author | Weiyun Zhan Haitao Li Xuefeng Wu Jingyue Zhang Chenxi Liu Dongming Zhang |
| author_facet | Weiyun Zhan Haitao Li Xuefeng Wu Jingyue Zhang Chenxi Liu Dongming Zhang |
| author_sort | Weiyun Zhan |
| collection | DOAJ |
| description | With the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore, a prediction method based on neural network is proposed in this paper to improve the accuracy and reliability of reserve upgrade prediction. In order to achieve this goal, by collecting the relevant data of natural gas exploration and development in Sichuan Basin, including geological parameters, production parameters and other indicators, and processing and analyzing the data, the relevant characteristics of reserves increase are extracted. Then, a neural network model based on multi-layer perceptron (MLP) is constructed and trained and optimized using backpropagation algorithm. The results show that the prediction accuracy of the constructed neural network model can reach more than 90% and can effectively predict the reserve upgrading. Experiments show that the model has high accuracy and reliability, and is significantly better than the traditional prediction methods. The method has good stability and reliability, and is suitable for a wider range of natural gas fields. |
| first_indexed | 2024-03-08T11:34:31Z |
| format | Article |
| id | doaj.art-9dc8225e752f4898897556d994eb7d2a |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-03-08T11:34:31Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-9dc8225e752f4898897556d994eb7d2a2024-01-25T14:02:32ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-08-011110.3389/feart.2023.12534951253495Research on neural network prediction method for upgrading scale of natural gas reservesWeiyun Zhan0Haitao Li1Xuefeng Wu2Jingyue Zhang3Chenxi Liu4Dongming Zhang5Exploration and Development Research Institute of PetroChina Southwest Oil and Gas Field Company, Chengdu, ChinaExploration and Development Research Institute of PetroChina Southwest Oil and Gas Field Company, Chengdu, ChinaExploration and Development Research Institute of PetroChina Southwest Oil and Gas Field Company, Chengdu, ChinaExploration and Development Research Institute of PetroChina Southwest Oil and Gas Field Company, Chengdu, ChinaCollege of Resources and Security, Chongqing University, Chongqing, ChinaCollege of Resources and Security, Chongqing University, Chongqing, ChinaWith the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore, a prediction method based on neural network is proposed in this paper to improve the accuracy and reliability of reserve upgrade prediction. In order to achieve this goal, by collecting the relevant data of natural gas exploration and development in Sichuan Basin, including geological parameters, production parameters and other indicators, and processing and analyzing the data, the relevant characteristics of reserves increase are extracted. Then, a neural network model based on multi-layer perceptron (MLP) is constructed and trained and optimized using backpropagation algorithm. The results show that the prediction accuracy of the constructed neural network model can reach more than 90% and can effectively predict the reserve upgrading. Experiments show that the model has high accuracy and reliability, and is significantly better than the traditional prediction methods. The method has good stability and reliability, and is suitable for a wider range of natural gas fields.https://www.frontiersin.org/articles/10.3389/feart.2023.1253495/fullnatural gas in sichuan basinreserve upgradecluster analysisanalytic hierarchy processneural network prediction |
| spellingShingle | Weiyun Zhan Haitao Li Xuefeng Wu Jingyue Zhang Chenxi Liu Dongming Zhang Research on neural network prediction method for upgrading scale of natural gas reserves Frontiers in Earth Science natural gas in sichuan basin reserve upgrade cluster analysis analytic hierarchy process neural network prediction |
| title | Research on neural network prediction method for upgrading scale of natural gas reserves |
| title_full | Research on neural network prediction method for upgrading scale of natural gas reserves |
| title_fullStr | Research on neural network prediction method for upgrading scale of natural gas reserves |
| title_full_unstemmed | Research on neural network prediction method for upgrading scale of natural gas reserves |
| title_short | Research on neural network prediction method for upgrading scale of natural gas reserves |
| title_sort | research on neural network prediction method for upgrading scale of natural gas reserves |
| topic | natural gas in sichuan basin reserve upgrade cluster analysis analytic hierarchy process neural network prediction |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1253495/full |
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