Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN
Coalbed methane resources are extensively developed using vertical wells, with controlled-pressure and controlled-water drainage systems. The flowing bottom hole pressure is a crucial parameter in the design of drainage schemes and equipment selection. Therefore, it is of great significance to predi...
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Editorial Department of Petroleum Reservoir Evaluation and Development
2025-04-01
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| Series: | Youqicang pingjia yu kaifa |
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| Online Access: | https://red.magtech.org.cn/fileup/2095-1426/PDF/1743493898202-848563259.pdf |
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| author | YU Yang, DONG Yintao, LI Yunbo, BAO Yu, ZHANG Lixia, SUN Hao |
| author_facet | YU Yang, DONG Yintao, LI Yunbo, BAO Yu, ZHANG Lixia, SUN Hao |
| author_sort | YU Yang, DONG Yintao, LI Yunbo, BAO Yu, ZHANG Lixia, SUN Hao |
| collection | DOAJ |
| description | Coalbed methane resources are extensively developed using vertical wells, with controlled-pressure and controlled-water drainage systems. The flowing bottom hole pressure is a crucial parameter in the design of drainage schemes and equipment selection. Therefore, it is of great significance to predict the flowing bottom hole pressure for vertical coalbed methane wells. To conveniently and accurately forecast the flowing bottom hole pressure of vertical coalbed methane and guide their pressure control and drainage, a Backpropagation Neural Network (BPNN) algorithm from the field of machine learning was introduced. Additionally, the Sparrow Search Algorithm (SSA) was improved. These were coupled to establish a forecasting model for flowing bottom hole pressure based on the improved SSA-BPNN approach. Five routinely measured parameters that influence flowing bottom hole pressure were selected as the input parameters for the prediction model, with corresponding bottom hole pressure values as the output parameters. A total of 600 sets of field-measured data were partitioned into training, validation, and testing datasets to develop and validate the forecasting model for vertical coalbed methane wells. The validation set showed that the mean absolute percentage errors for the BPNN model and the Improved SSA-BPNN model on the validation set were 3.10% and 0.53%, respectively. This demonstrated that coupling the Improved SSA and BPNN effectively overcame the propensity of BPNN to converge to local optima, thereby improving the prediction accuracy of flowing bottom hole pressure in a vertical coalbed methane well. Furthermore, the improved SSA-BPNN model was compared with the Genetic Algorithm-Support Vector Regression (GA-SVR) method and the physical model-based analytical method. The results revealed that the mean absolute percentage errors for these three different models were 1.318%, 4.971%, and 18.156%, respectively. The Improved SSA-BPNN model had the lowest error, and its prediction accuracy significantly improved when the flowing bottom hole pressure was low, demonstrating its higher accuracy and strong applicability. The Improved SSA-BPNN model requires only five input parameters, reducing the complexity of input and calculation parameters. It does not require consideration of the fluid distribution within the wellbore and can cover all stages of drainage, maintaining high accuracy across different pressure ranges. |
| format | Article |
| id | doaj-art-ecb1e052e9ab488b98c1dda5c7cf26cd |
| institution | OA Journals |
| issn | 2095-1426 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Editorial Department of Petroleum Reservoir Evaluation and Development |
| record_format | Article |
| series | Youqicang pingjia yu kaifa |
| spelling | doaj-art-ecb1e052e9ab488b98c1dda5c7cf26cd2025-08-20T02:03:07ZzhoEditorial Department of Petroleum Reservoir Evaluation and DevelopmentYouqicang pingjia yu kaifa2095-14262025-04-0115225025610.13809/j.cnki.cn32-1825/te.2025.02.009Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNNYU Yang, DONG Yintao, LI Yunbo, BAO Yu, ZHANG Lixia, SUN Hao01. PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China;2. CNOOC Research Institute Co., Ltd., Beijing 100028, ChinaCoalbed methane resources are extensively developed using vertical wells, with controlled-pressure and controlled-water drainage systems. The flowing bottom hole pressure is a crucial parameter in the design of drainage schemes and equipment selection. Therefore, it is of great significance to predict the flowing bottom hole pressure for vertical coalbed methane wells. To conveniently and accurately forecast the flowing bottom hole pressure of vertical coalbed methane and guide their pressure control and drainage, a Backpropagation Neural Network (BPNN) algorithm from the field of machine learning was introduced. Additionally, the Sparrow Search Algorithm (SSA) was improved. These were coupled to establish a forecasting model for flowing bottom hole pressure based on the improved SSA-BPNN approach. Five routinely measured parameters that influence flowing bottom hole pressure were selected as the input parameters for the prediction model, with corresponding bottom hole pressure values as the output parameters. A total of 600 sets of field-measured data were partitioned into training, validation, and testing datasets to develop and validate the forecasting model for vertical coalbed methane wells. The validation set showed that the mean absolute percentage errors for the BPNN model and the Improved SSA-BPNN model on the validation set were 3.10% and 0.53%, respectively. This demonstrated that coupling the Improved SSA and BPNN effectively overcame the propensity of BPNN to converge to local optima, thereby improving the prediction accuracy of flowing bottom hole pressure in a vertical coalbed methane well. Furthermore, the improved SSA-BPNN model was compared with the Genetic Algorithm-Support Vector Regression (GA-SVR) method and the physical model-based analytical method. The results revealed that the mean absolute percentage errors for these three different models were 1.318%, 4.971%, and 18.156%, respectively. The Improved SSA-BPNN model had the lowest error, and its prediction accuracy significantly improved when the flowing bottom hole pressure was low, demonstrating its higher accuracy and strong applicability. The Improved SSA-BPNN model requires only five input parameters, reducing the complexity of input and calculation parameters. It does not require consideration of the fluid distribution within the wellbore and can cover all stages of drainage, maintaining high accuracy across different pressure ranges.https://red.magtech.org.cn/fileup/2095-1426/PDF/1743493898202-848563259.pdf|coalbed methane|sparrow search algorithm|neural network|bottom hole flowing pressure|prediction model |
| spellingShingle | YU Yang, DONG Yintao, LI Yunbo, BAO Yu, ZHANG Lixia, SUN Hao Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN Youqicang pingjia yu kaifa |coalbed methane|sparrow search algorithm|neural network|bottom hole flowing pressure|prediction model |
| title | Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN |
| title_full | Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN |
| title_fullStr | Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN |
| title_full_unstemmed | Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN |
| title_short | Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN |
| title_sort | research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved ssa bpnn |
| topic | |coalbed methane|sparrow search algorithm|neural network|bottom hole flowing pressure|prediction model |
| url | https://red.magtech.org.cn/fileup/2095-1426/PDF/1743493898202-848563259.pdf |
| work_keys_str_mv | AT yuyangdongyintaoliyunbobaoyuzhanglixiasunhao researchonpredictionofbottomholeflowingpressureforverticalcoalbedmethanewellsbasedonimprovedssabpnn |