Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs
Abstract In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21075-w |
| _version_ | 1811194883887595520 |
|---|---|
| author | Rakesh Kumar Pandey Shrey Aggarwal Griesha Nath Anil Kumar Behzad Vaferi |
| author_facet | Rakesh Kumar Pandey Shrey Aggarwal Griesha Nath Anil Kumar Behzad Vaferi |
| author_sort | Rakesh Kumar Pandey |
| collection | DOAJ |
| description | Abstract In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their simultaneous accomplishment is always challenging. This work combines genetic algorithm optimization and artificial neural networks to identify and characterize homogeneous reservoir systems from well-testing data automatically. A total of eight prediction models, including two classifiers and six regressors, have been trained. The simulated well-test pressure derivatives with varying noise percentages comprise the training samples. The feature selection and hyperparameter tuning have been performed carefully using the genetic algorithm to enhance the prediction accuracy. The models were validated using nine simulated and one real-field test case. The optimized classifier identifies all the reservoir models with a classification accuracy higher than 79%. In addition, the statistical analysis approves that the optimized regressors accurately perform the reservoir characterization with mean relative errors of lower than 4.5%. The minimized manual interference reduces human bias, and the models have significant noise tolerance for practical applications. |
| first_indexed | 2024-04-12T00:34:49Z |
| format | Article |
| id | doaj.art-0688b95c80014b09ad06ce7bcd9ac4b7 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T00:34:49Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0688b95c80014b09ad06ce7bcd9ac4b72022-12-22T03:55:11ZengNature PortfolioScientific Reports2045-23222022-10-0112111610.1038/s41598-022-21075-wMetaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirsRakesh Kumar Pandey0Shrey Aggarwal1Griesha Nath2Anil Kumar3Behzad Vaferi4Department of Petroleum and Energy Studies, School of Engineering and Technology, DIT UniversityData Science Research Group, School of Computing, DIT UniversityData Science Research Group, School of Computing, DIT UniversityData Science Research Group, School of Computing, DIT UniversityDepartment of Chemical Engineering, Shiraz Branch, Islamic Azad UniversityAbstract In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their simultaneous accomplishment is always challenging. This work combines genetic algorithm optimization and artificial neural networks to identify and characterize homogeneous reservoir systems from well-testing data automatically. A total of eight prediction models, including two classifiers and six regressors, have been trained. The simulated well-test pressure derivatives with varying noise percentages comprise the training samples. The feature selection and hyperparameter tuning have been performed carefully using the genetic algorithm to enhance the prediction accuracy. The models were validated using nine simulated and one real-field test case. The optimized classifier identifies all the reservoir models with a classification accuracy higher than 79%. In addition, the statistical analysis approves that the optimized regressors accurately perform the reservoir characterization with mean relative errors of lower than 4.5%. The minimized manual interference reduces human bias, and the models have significant noise tolerance for practical applications.https://doi.org/10.1038/s41598-022-21075-w |
| spellingShingle | Rakesh Kumar Pandey Shrey Aggarwal Griesha Nath Anil Kumar Behzad Vaferi Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs Scientific Reports |
| title | Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs |
| title_full | Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs |
| title_fullStr | Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs |
| title_full_unstemmed | Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs |
| title_short | Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs |
| title_sort | metaheuristic algorithm integrated neural networks for well test analyses of petroleum reservoirs |
| url | https://doi.org/10.1038/s41598-022-21075-w |
| work_keys_str_mv | AT rakeshkumarpandey metaheuristicalgorithmintegratedneuralnetworksforwelltestanalysesofpetroleumreservoirs AT shreyaggarwal metaheuristicalgorithmintegratedneuralnetworksforwelltestanalysesofpetroleumreservoirs AT grieshanath metaheuristicalgorithmintegratedneuralnetworksforwelltestanalysesofpetroleumreservoirs AT anilkumar metaheuristicalgorithmintegratedneuralnetworksforwelltestanalysesofpetroleumreservoirs AT behzadvaferi metaheuristicalgorithmintegratedneuralnetworksforwelltestanalysesofpetroleumreservoirs |