Gas Allocation Optimization Research Articles

A new gas lift allocation method in the IoT environment using a hybrid optimization algorithm

In the realm of petroleum extraction, well productivity declines as reservoirs deplete, eventually reaching a point where continued extraction becomes economically unfeasible. To counteract this, artificial lift techniques are employed, with gas injection being a prevalent method. Ideally, unrestricted gas injection could maximize oil output. However, gas scarcity necessitates judicious resource management to optimize oil production while minimizing gas usage. Gas injection serves to alleviate hydrostatic pressure within wells, thereby enhancing oil recovery. Conventional gas allocation strategies often prove inadequate when confronted with the complex, non-linear constraints of real-world scenarios, particularly under gas supply limitations. This research introduces an innovative approach to gas allocation optimization, leveraging Internet of Things (IoT) technology in conjunction with advanced computational methods. The study melds two optimization algorithms: Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO). This hybrid technique harnesses IoT capabilities for real-time data acquisition and processing, enabling more precise and adaptive optimization. The proposed methodology incorporates PSO’s individual and collective learning mechanisms into the ASO framework, accelerating the solution refinement process. Additionally, it introduces dynamic parameters to balance broad exploration with focused exploitation of the solution space. The algorithm’s efficacy is further enhanced by implementing an adaptive force constant for each “atom” (solution candidate), which evolves based on the atom’s performance over successive iterations. Empirical evaluation of this novel approach demonstrated significant improvements in both energy efficiency and gas utilization. Specifically, the hybrid method achieved average reductions of 12.12% in energy consumption and 18.05% in gas injection volume compared to existing techniques. Also, the results showed that battery life and cost are better than the other methods and have been improved by an average of 7.67% and 9.48%, respectively.

A synergy between the genetic algorithm and simulated annealing in a gas allocation optimization problem

A synergy between the genetic algorithm and simulated annealing in a gas allocation optimization problem

Developing an improved approach to solving a new gas lift optimization problem

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.

Stabilizing gas lift optimization with different amounts of available lift gas

One of the problems that sometimes occurs in gas lift operations is the instability phenomenon. This phenomenon reduces the oil production and damages down hole and surface facilities. There are many studies on different aspects of this problem but none of them have focused on this phenomenon in a gas allocation optimization problem. Here, as a new approach, instability has been prevented by considering it in the design stage of a gas allocation optimization problem. In this paper, six wells of the Iranian oil field were selected and different amounts of lift gas were allocated among them in a way that the total production was maximized. In each case, the effect of the considering stability was discussed. In addition, about 2000 other wells were investigated to see whether there is any safe situation in which gas allocation optimization can be carried out without any concern about instability. The results indicated that in any case (no matter how much gas is available), it is probable that the optimum point be in an unstable region (of course with different probability). However, by using the method of this study, considering instability as a constraint for the optimizer can lead the optimum point to a stable region with a very small production loss.

Preventing Instability Phenomenon in Gas-lift Optimization

One of the problems that sometimes occur in gas allocation optimization is instability phenomenon. This phenomenon reduces the oil production and damages downhole and surface facilities. Different works have studied the stability and suggested some solutions to override it, but most of them (such as making the well intelligent) are very expensive and thus they are not applicable to many cases. In this paper, as a new approach, the stability has been studied in gas allocation optimization problems. To prevent the instability, instability has been assumed as a constraint for the optimizer and then the optimizer has been run. For the optimization, first a genetic algorithm and then a hybrid of genetic algorithm and Newton-Quasi have been used, and their results are compared to ensure the good performance of the optimizer; afterwards, the effect of adding the instability constraint to the problem on production reduction have been discussed. The results show that the production loss with adding this constraint to the system is very small and this method does not need any additional and expensive facilities for preventing the instability. Therefore, the new method is applicable to different problems.

Improving Gas Allocation Optimization to a Group of Wells in Gas Lift Using an Efficient Hybrid Genetic Algorithm (HGA)

One of the most commonly used artificial lift methods in the petroleum industry is continuous gas lift. In continuous gas lift, gas at suitable pressure is injected in an appropriate depth into the tubing to gasify the oil column and, thus, facilitate the production. In gas lift, by increasing the gas injection rate into the well, the oil production rate enhances. At a point that is an optimal point of the well, by increasing gas injection rate, the oil production rate declines. At this point, the well produces the most oil. If there is no limitation in the total amount of available gas, a sufficient amount of gas could be injected into each well to get the maximum amount of production. However, often the total amount of available gas is insufficient to inject enough gas to an optimal point of each well and to reach the maximum oil production for each well. Therefore, allocating an optimum amount of gas to each well to obtain the field maximum oil production rate is necessary. In this work, a hybrid genetic algorithm was used to allocate the optimum amount of gas to a group of wells for three fields with a different number of wells. Based upon the fields' oil total production rate resulted from the gas allocation to the wells, the hybrid genetic algorithm shows better gas allocation to the wells in comparison with previous works with other methods.

Genetic Algorithms and Ant Colony Approach for Gas-lift Allocation Optimization

Continuous gas-lift is one of the most commonly practiced artificial lift techniques. It assists production enhancement by continuous injection of high-pressure gas into the well tubing, which lightens the oil column. Either gas limitation or compressor capacity makes it impossible to make all the network wells produce at the optimum rate; hence the need to determine the optimal gas distribution. Gas allocation optimization is a type of nonlinear function maximization with gas injection rates as decision variables subject to physical restrictions. Various optimization methods are applied in previous works among which genetic algorithm (GA) has proposed the best efficiency for large networks. In this work, various methods are performed as a comparison to GA. Besides, ant colony optimization (ACO) is applied to the network as a new optimization tool in oil industry, as a possible alternative for GA, already proved its capability in the optimization of water distribution networks. The literature demonstrated the application capability of GA and ACO for gas-lift allocation optimization in small networks. The results proved an availability of GA and ACO for this problem, and it showed the applicability to the optimization of large-scale networks. The results are compared to similar calculations in the literature by other optimization techniques, which show promising agreement.

Global Optimization of Gas Allocation to a Group of Wells in Artificial Lift Using Nonlinear Constrained Programming

Continuous flow gas lift is one of the most common artificial lift methods widely used in the oil industry. A continuous volume of high-pressure gas is injected as deep as possible into the tubing, to gasify the oil column, and thus facilitate the production. If there is no restriction in the amount of injection gas available, sufficient gas can be injected into each oil well to reach maximum production. However, the injection gas available is generally insufficient. An inefficient gas allocation in a field with limited gas supply reduces the revenues, since excessive gas injection is expensive due to the high gas prices and compressing costs. Therefore, it is necessary to assign the injection gas into each well in optimal form to obtain the field maximum oil production rate. The gas allocation optimization can be considered as a maximization of a nonlinear function, which models the total oil production rate for a group of wells. The variables or unknowns for this function are the gas injection rates for each well, which are subject to physical restrictions. In this work a nonlinear optimization technique, based on an objective function with constraints, was implemented to find the optimal gas injection rates. A new mathematical fit to the gas-lift performance curve (GLPC) is presented and the numeric results of the optimization are given and compared with those of other methods published in the specialized literature. The GLPC can be either measured in the field, or alternatively generated by computer simulations, by mean of nodal analysis. The optimization technique proved fast convergence and broad application.