Multi-Objective Gas Lift Optimization using Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II)
V Sreeharsha1, Pulak Jawaria2, M Ragavendra3, B Madhuri4, Vishesh Bhadaria5
1V Sreeharsha have position of asst. professor in vignan’s foundation for science, technology and research. He has done his master degree from IIT Dhanbad.
2Pulak jawaria has done master degree from IIT Dhanbad.
3Vishesh Bhadaria, has master degree from IIT Dhanbad.
4M Ragavendra, has master degree from IIT Dhanbad.
5B Madhuri, has master degree from IIT Dhanbad.

Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9737-9740 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9189118419/2019©BEIESP | DOI: 10.35940/ijrte.D9189.118419

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Abstract: In petroleum industry, gas lift optimization is the most important for evaluating the reservoir. By improving the gas lift operation we can save money and time which we spend on the reservoir for effective production. The mainly accepted scenario of gas lift is to maximize production by using minimized cost infrastructure. If the production rate is increased, then the cost of oil production also increases due to the increase in surface facilities and increase in cost of gas compression to higher pressures. The production rate and production cost during gas lift are mutually conflicting in nature i.e., if anyone desires to increase the oil production rate, then at the same time it is difficult to minimize the cost of production. Therefore, this is an ideal candidate for multi-objective optimization study, where production rate needs to maximized while minimizing the cost of production. The oil production rate is calculated using nodal analysis of inflow performance and outflow performance curve while the production cost is calculated using the brake horsepower requirement of the compressor. Oil production rate during a gas lift operation can be defined as a function of various factors like (i) depth of gas injection, (ii) gas injection rate (iii) gas lift injection pressure, (iv) wellhead pressures, (v) bottom hole pressure, (vi) tubing size, (vii) surface choke size/wellhead pressure. Production cost mainly depends on the cost of gas compression which further depends on the pressure up to which gas has to be compressed in the annulus so that the gas lift valve at the bottom of the well opens. The opening of gas lift valve depends on the bottom hole pressure in the tubing i.e. the density of mixture present inside the tubing. The multi-objective gas lift optimization is carried out using multi-objective evolutionary algorithms (EAs) that use non-dominated sorting called elitist non-dominated sorting genetic algorithm (NSGA-II). In this project, we aim to find the optimum values of the decision parameters i.e. gas injection rate and wellhead pressure, for which oil production rate would be maximized while minimizing the cost of oil production.
Keywords: Gas Lift, Nodal Analysis, Multi-Objective Optimization, Pareto-Optimal Solutions, Genetic Algorithms.
Scope of the Article: Discrete Optimization.