| Autor: |
ABESHI, P. U., OLIOMOGBE, T. I., EMEGHA, J. O., ADEYEYE, V. A., ATUNWA Y. O., Y. O. |
| Zdroj: |
Journal of Applied Sciences & Environmental Management; Nov2023, Vol. 27 Issue 11, p2629-2635, 7p |
| Abstrakt: |
Reservoirs of natural gas and gas condensate have been proposed as a potential for providing affordable and cleaner energy sources to the global population growth and industrialization expansion simultaneously. This work evaluates reservoir simulation for production optimization using Deep Neural network - artificial neural network (DNN-ANN) model to predict the dew point pressure in gas condensate reservoirs from Field-X in the Niger Delta Region of Nigeria. The dew-point pressure (DPP) of gas condensate reservoirs was estimated as a function of gas composition, reservoir temperature, molecular weight and specific gravity of heptane plus percentage. Results obtained show that the mean relative error (MRE) and R-squared (R² ) are 0.99965 and 3.35%, respectively, indicating that the model is excellent in predicting DPP values. The Deep Neural Network - Artificial Neural Network (DNN-ANN) model is also evaluated in comparison to earlier models created by previous authors. It was recommended that the DNN - ANN model developed in this study could be applied to reservoir simulation and modeling well performance analysis, reservoir engineering problems and production optimization. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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