Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates
It is economically and technically essential to promptly and accurately estimate the dew point pressure (DPP) of gas condensate to, for example, characterize fluids, evaluate the performance of reservoirs, plan and develop reservoirs for gas condensates, and design/optimize a production system. Inde...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1929350 |
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| author | Luo Han Saeed Sarvazizi |
| author_facet | Luo Han Saeed Sarvazizi |
| author_sort | Luo Han |
| collection | DOAJ |
| description | It is economically and technically essential to promptly and accurately estimate the dew point pressure (DPP) of gas condensate to, for example, characterize fluids, evaluate the performance of reservoirs, plan and develop reservoirs for gas condensates, and design/optimize a production system. Indeed, it is difficult to experimentally explore the DPP. Furthermore, experimental tests are time-consuming and complicated. Therefore, it is required to develop an accurate, reliable DPP estimation framework. This paper introduces artificial neural network (ANN) models coupled with optimization algorithms, including a genetic algorithm (GA) and particle swarm optimization (PSO), for DPP estimation. A total of 721 data points were employed to train and test the algorithm. In addition, the outlier data were identified and excluded. The root-mean-squared error (RMSE) and the coefficient of determination (R2) were calculated to be 230.42 and 0.982 for the PSO-ANN model and 0.0022 and 0.997 for the GA-ANN model, respectively. The model estimates were found to be in good agreement with the experimental dataset. Therefore, it can be said that the proposed method is efficient and effective. |
| format | Article |
| id | doaj-art-6a5b74ef06194fd4bc6079bafb55a5f6 |
| institution | Kabale University |
| issn | 1687-8078 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-6a5b74ef06194fd4bc6079bafb55a5f62025-08-20T03:33:39ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/1929350Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas CondensatesLuo Han0Saeed Sarvazizi1School of ComputerDepartment of Petroleum EngineeringIt is economically and technically essential to promptly and accurately estimate the dew point pressure (DPP) of gas condensate to, for example, characterize fluids, evaluate the performance of reservoirs, plan and develop reservoirs for gas condensates, and design/optimize a production system. Indeed, it is difficult to experimentally explore the DPP. Furthermore, experimental tests are time-consuming and complicated. Therefore, it is required to develop an accurate, reliable DPP estimation framework. This paper introduces artificial neural network (ANN) models coupled with optimization algorithms, including a genetic algorithm (GA) and particle swarm optimization (PSO), for DPP estimation. A total of 721 data points were employed to train and test the algorithm. In addition, the outlier data were identified and excluded. The root-mean-squared error (RMSE) and the coefficient of determination (R2) were calculated to be 230.42 and 0.982 for the PSO-ANN model and 0.0022 and 0.997 for the GA-ANN model, respectively. The model estimates were found to be in good agreement with the experimental dataset. Therefore, it can be said that the proposed method is efficient and effective.http://dx.doi.org/10.1155/2022/1929350 |
| spellingShingle | Luo Han Saeed Sarvazizi Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates International Journal of Chemical Engineering |
| title | Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates |
| title_full | Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates |
| title_fullStr | Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates |
| title_full_unstemmed | Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates |
| title_short | Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates |
| title_sort | applying optimized ann models to estimate dew point pressure of gas condensates |
| url | http://dx.doi.org/10.1155/2022/1929350 |
| work_keys_str_mv | AT luohan applyingoptimizedannmodelstoestimatedewpointpressureofgascondensates AT saeedsarvazizi applyingoptimizedannmodelstoestimatedewpointpressureofgascondensates |