A combination of artificial neural network and genetic algorithm to optimize gas injection: A case study for EOR applications
| Autor: | Arash Javadi, Vahid Sheikhol Moluki, Omid Mohammadzadeh, Aghil Moslemizadeh, Sohrab Zendehboudi, Nader Fathianpour |
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| Rok vydání: | 2021 |
| Předmět: |
Optimization problem
Artificial neural network business.industry Computer science Process (computing) 02 engineering and technology Maximization 010402 general chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Atomic and Molecular Physics and Optics 0104 chemical sciences Electronic Optical and Magnetic Materials Volume (thermodynamics) Genetic algorithm Materials Chemistry Production (economics) Enhanced oil recovery Physical and Theoretical Chemistry 0210 nano-technology Process engineering business health care economics and organizations Spectroscopy |
| Zdroj: | Journal of Molecular Liquids. 339:116654 |
| ISSN: | 0167-7322 |
| DOI: | 10.1016/j.molliq.2021.116654 |
| Popis: | A considerable volume of oil remains in the reservoir after terminating the primary and secondary oil recovery stages. It is the target of enhanced oil recovery (EOR) processes to increase the overall oil displacement efficiency. Gas injection process is the most commonly used technique in EOR. There has always been a demand for optimizing the gas injection process through modifying the determining factors such as injection/production well placement, injection rate, composition of the injected gas phase, to name a few. In this study, a combination of artificial neural networks (ANN) and genetic algorithm (GA) was used to discover and then optimize the connections between different properties and variables of a reservoir which is undergoing a gas injection process. A commercial reservoir simulator was used to simulate the production performance of the target reservoir. A new objective function was also developed to optimize the gas injection process based on the final profits gained. According to the results, production maximization was not the best decision since it was not the most profitable alternative. However, a profit-based optimization considering the costs of gas injection and the oil price was proved to be a better solution to the optimization problem. For a five-year production period, employing the profit-based optimization for well productivity index calculation resulted in an earned profit of 5.7 × 109 USD, achieved through injection of 203,600 MMSCFD of gas. On the other hand, production maximization approach led to a lesser earned profit of 5.39 × 109 USD due to injection of much greater volume of gas (310,000 MMSCFD). The results of the ANN modelling demonstrated the ability of the hybrid smart model in predicting production performance of the target reservoir under gas injection process. In addition, the ANN model was capable of accurately predicting noise-containing data which makes it a highly reliable candidate to apply on real field data. |
| Databáze: | OpenAIRE |
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