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Gas lift technology increases oil production rate by injection of compressed gas into the lower section of tubing through the casing-tubing annulus and an orifice installed on the tubing string. To achieve optimum recovery, operation must begin at the optimum time, in addition to inject optimum gas rate. In this work, we develop a new approach to consider the time factor in the gas lift optimization process. A piecewise cubic Hermite function is used to model the gas lift performance. The optimization procedure for gas allocation to several wells is achieved using the Genetic algorithm approach. The developed model was used to study the effect of gas lift initiation time on the reservoir life and net present value. Our calculations showed that the initiation time has a noticeable influence on the optimization procedure and should be considered a major factor for gas allocation problems in real fields.

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Reservoir simulation plays a vital role as a diagnostics tool to better understand and predict a reservoir’s behaviour. The primary purpose of running a reservoir simulation is to replicate reservoir performance under different production conditions; therefore, the development of a reliable and fast dynamic reservoir model is a priority for the industry. In each simulation, the reservoir is divided into millions of cells, with fluid and rock attributes assigned to each cell. Based on these attributes, flow equations are solved through numerical methods, resulting in an excessively long processing time. Given the recent progress in machine learning methods, this study aimed to further investigate the possibility of using deep learning in reservoir simulations. Throughout this paper, we used deep learning to build a data-driven simulator for both 1D oil and 2D gas reservoirs. In this approach, instead of solving fluid flow equations directly, a data-driven model instantly predicts the reservoir pressure using the same input data of a numerical simulator. Datasets were generated using a physics-based simulator. It was found that for the training and validation sets, the mean absolute percentage error (MAPE) was less than 15.1% and the correlation coefficient, R2, was more than 0.84 for the 1D oil reservoirs, while for the 2D gas reservoir MAPE < 0.84% and R2 ≈1. Furthermore, the sensitivity analysis results confirmed that the proposed approach has promising potential (MAPE < 5%, R2 > 0.9). The results agreed that the deep learning based, data-driven model is reasonably accurate and trustworthy when compared with physics-derived models.