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Gas injection is a well-known method for enhancing oil recovery (EOR). The utilization of greenhouse gases, such as carbon dioxide (CO2) or flue gas, offers the dual advantage of reducing greenhouse gas emissions while potentially enhancing the sweep efficiency in oil recovery. Nevertheless, one of the notable challenges encountered when using these gases is the precipitation and deposition of asphaltenes, leading to formation damage and a decrease in reservoir permeability, particularly in the case of light oil reservoirs. In this study, CO2 and flue gas were injected into an elongated core sample comprising four individual core plugs under reservoir conditions to displace the light live oil. The recovery factor and asphaltene deposition along the core holder were assessed and compared as two crucial parameters within the gas injection scenario. Our results indicate a significantly higher recovery factor of 86% achieved with CO2 injection compared to 36% with flue gas injection, attributable to differences in their interfacial tension and miscibility. However, the CO2 injection method exhibits more pronounced formation damage. Individual assessment of each core plug reveals that permeability impairment is most acute in the initial two core plugs, situated closer to the injection face of the extended core. These findings enhance our understanding of the mechanisms contributing to permeability impairment resulting from asphaltene deposition during CO2 and flue gas injection for EOR. [ABSTRACT FROM AUTHOR]

Base gas is considered as an important factor in the storage operation as it remains permanently in the reservoir and maintains the reservoir pressure along the production cycle. Depending on the reservoir under consideration, base gas may occupy as little as 15% or as much as 75% of the total underground gas storage (UGS) reservoirs. Providing and injecting the cushion gas has the most contribution to the cost of the storage operations. Therefore, part of the base gas can be replaced by a cost-effective gas such as nitrogen, flue gas or air to reduce the costs of the investment. Some degree of mixing takes place when two miscible gases come into contact with one another that affects the quality of the produced natural gas. Therefore, the process needs to be studied and controlled. In this study, the feasibility of underground gas storage and the substitution of the base gas by a cheaper gas, i.e., nitrogen, flue gas, and air, are investigated in a partially depleted dry gas reservoir with very low initial pressure. To do so, a comparative study is performed among nitrogen, flue gas and air as the alternative gases to the base gas. In addition, the effect of flue gas composition on the performance of base gas replacement and ultimate gas recovery is investigated. Pure CO2 is considered as flue gas with zero mole% N2. In the end, the effect of the reservoir properties on mixing between the gases is studied. The results indicated that it is possible to substitute 24.8% of the base gas by nitrogen to obtain a 16.2% increase in the gas recovery of the reservoir. In this case, the ultimate recovery reaches 50.90%. Using flue gas as the alternative gas, the results showed a 15.6% increase in the gas recovery of the reservoir, obtained by substituting 23.9% of the base gas. The ultimate recovery using flue gas is 50.31%. According to the results, flue gas can be used as an appropriate option to replace the base gas of the UGS reservoir under consideration, and hence, there would be no more need for separation and purification of N2 and CO2. Increasing the CO2 composition in the flue gas up to 46.6 mole% leads to a decrease in the base gas replacement amount. When the composition increases above 46.6 mole%, the amount of the replaced gas does not change. However, in this composition range, more flue gas is injected into the reservoir, which has environmental advantages. The highest injection rate of the flue gas is obtained when the flue gas contains 100 mole% of CO2. The main problem in using air as the base gas is the high viscosity of air which requires a high injection pressure. According to the results, using air as the replacement gas, 21.3% of the base gas is substituted by air. In this case, gas recovery increases by 13.9% with respect to the reservoir depletion scenario and the ultimate recovery reaches 48.62%. [ABSTRACT FROM AUTHOR]

Ideally, if there is no limitation in the available resources of gas in the gas lift operation, the adequate amount of gas can be injected into the reservoir to have the highest production rate. However, the amount of available gas and the capacity of the compressors are limited and it is required to optimize the gas injection into each well to have the highest recovery. Most of the former studies have been done by complex optimization algorithms and neural networks which require programming languages and a huge amount of time. In this study, we tried to optimize the amount of the injected gas in one of the heavy oil reservoirs in the west of Iran by the use of excel solver software. Then, the results are compared with the famous methods of optimization by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Additionally, to improve the accuracy of calculation of the amount of oil production versus the amount of gas injection to the well, the empirical correlations are modified to fit the gas lift performance curves and a new correlation is developed. The results show that the model developed by the authors have the highest accuracy in comparison to other methods. Moreover, the results show that the accuracy of excel solver software is high enough in comparison to GA and PSO algorithm. Abbreviations: ANN - Artificial Neural Network; ARE - Absolute Relative Error; BR - Bayesian Regularization; D - Day; GA - Genetic Algorithm; GLPC - Gas Lift Performance Curve; GOR - Gas Oil Ratio; IPR - Inflow Performance Relationship; LM - Levenberg–Marquardt; MD - Measured Depth; MINLP - Mixed Integer Nonlinear Programming; MMSCF - Million standard Cubic Feet; MSCF - Thousand Standard Cubic Feet; P.I - Productivity Index; ppm - parts per million; PSO - Particle Swarm Optimization; PVT - Pressure-Volume-Temperature; SQP - Sequential Quadratic Programming; SSE - Sum of Squared Errors; SST - Total Sum of Squares; SSSV - Sub Surface Safety Valve; STB - Stock Tank Barrel; TBG - Tubing; TLBO - Teaching-Learning Based Optimization; TVD - True Vertical Depth; WPC - Well Production Curve. [ABSTRACT FROM AUTHOR]