Computational analysis demonstrates accurate gas-oil ratio prediction in gas lift oil wells using neural networks trained on hybrid datasets, highlighting improved production monitoring.
The accurate prediction of the Gas-Oil Ratio (GOR) is crucial in optimizing oil production and ensuring the longevity of oil wells. This study evaluates the effectiveness of Neural Networks in predicting GOR for oil wells using gas lift production. We demonstrate that Neural Networks, particularly when trained with a mix of real and simulated data, show great promise for precise and rapid GOR predictions. To validate our approach, we analyzed data from real-world oil wells, employing Neural Networks for prediction. The results reveal that this method can predict GOR accurately and within a reasonable time frame, provided sufficient training data is available. Our findings offer valuable insights for oil well operators and engineers aiming to enhance their GOR prediction strategies.
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Amorim et al. (2024) studied this question.
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