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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]

In the face of the escalating global energy demand, the challenge lies in enhancing the extraction of oil from low-pressure underground reservoirs. The conventional artificial gas lift method is constrained by the limited availability of high-pressure gas for injection, which is essential for reducing hydrostatic bottom hole pressure and facilitating fluid transfer to the surface. This study proposes a novel 'smart gas' concept, which involves injecting a gas mixture with an optimized fraction of CO2 and N2 into each well. The research introduces a dual optimization strategy that not only determines the optimal gas composition but also allocates the limited available gas among wells to achieve multiple objectives. An extensive optimization process was conducted to identify the optimal gas injection rate for each well, considering the limited gas supply. The study examined the impact of reducing available gas from 20 to 10 MMSCFD and the implications of water production restrictions on oil recovery. The introduction of smart gas resulted in a 3.1% increase in overall oil production compared to using natural gas. The optimization of smart gas allocation proved effective in mitigating the decline in oil production, with a 25% reduction in gas supply leading to only a 10% decrease in oil output, and a 33% reduction resulting in a 26.8% decrease. The study demonstrates that the smart gas approach can significantly enhance oil production efficiency in low-pressure reservoirs, even with a substantial reduction in gas supply. It also shows that imposing water production limits has a minimal impact on oil production, highlighting the potential of smart gas in achieving environmentally sustainable oil extraction. Furthermore, the implementation of the smart gas approach aligns with global environmental goals by potentially reducing greenhouse gas emissions, thereby contributing to the broader objective of environmental sustainability in the energy sector. [ABSTRACT FROM AUTHOR]

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 replacement by a cheap gas is one of the approved methods to reduce the cost of investment in underground gas storage process. Maximizing the amount of replaceable base gas is an attractive issue. However, the amount of base gas replacement can be reduced due to some limiting factors. By changing the composition of the candidate base gas, it is possible to obtain a smart gas with an optimized composition through which all limiting factors are simultaneously satisfied. In the present study, the feasibility of this idea is investigated in a real depleted gas reservoir by flue gas as the candidate gas with the aid of GEM compositional simulator and proxy model. The proxy model and Excel Solver optimization tools are used to improve the speed and accuracy in determining the optimum composition of flue gas composition. In addition, the effect of flue gas composition on the reservoir pressure and gas deviation factor is analyzed during gas storage cycles. The results show that when the CO2 content of the flue gas exceeds a specific value, carbon dioxide effect is dominant, which increases the wetting effect of the flue gas. In this case, the simultaneous injection of flue gas and storage gas in odd cycles reduces the reservoir pressure and the reservoir gas deviation factor. The results indicate that it is possible to replace 28.4% of the base gas and reach an enhanced gas recovery of about 18.55% in the reservoir under study using the optimized replacing gas composition of 17.24% CO2 and 82.76% N2. [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]

The increased speed and accuracy in solving optimization problems of gas allocation in the gas lift process are of high importance. Solving gas allocation optimization problems generally involves two steps: (1) The gas lift performance curve (GLPC) fitting (gas lift modeling) and (2) optimizing the allocation of gas between wells. Therefore, in order to increase the speed and accuracy of solving gas allocation optimization problems, both steps need to be improved. In order to increase the accuracy of the first step, a new correlation was proposed in which, in addition to increasing the accuracy of fit, the optimization speed was improved by decreasing the number of constants used in the correlation. Besides, in order to improve the performance of the second step, water cycle optimization algorithm was used and the results obtained from this algorithm were compared with the results obtained from previous studies on teaching–learning-based optimization (TLBO) algorithm, continuous ant colony (CACO) algorithm, genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for solving the five-well Nishikiori index problem. The results suggested that the water cycle optimization algorithm has a very good performance in terms of convergence rate, non-capture at local optimum points and repeatability. Finally, as a new problem, the gas allocation between the wells of one of the heavy oil fields in the southwest of Iran was optimized with predetermined oil production rates. The goal of optimization was to obtain the minimum amount of gas required to produce the predetermined oil rates using the water cycle optimization algorithm. The results showed that optimization is of higher importance in lower oil production targets, resulting in higher additional oil production. [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]