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A case study of South Pars natural gas injection optimization in Ahvaz field oil wells
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Mogtaba Khani   , Ali asgar Esmailnia ketabi *1 , Ghodratollah Emamverdi , Hooshang Vesaliyan |
| 1- , aektabi@gmail.com |
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Abstract: (54 Views) |
Natural gas has evolved into a critically important strategic hydrocarbon resource in today's global energy landscape, serving not merely as a complementary fuel to oil but also playing a pivotal role in enhanced oil recovery (EOR) and production maintenance operations. In this study, we employ an integrated modeling framework developed using MATLAB, Eclipse, and PipeSim software to analyse the optimal utilization strategy for Phase 13 of South Pars Gas Field - Iran's largest gas reservoir - through gas injection into Ahvaz Oil Field. The research methodology incorporates multiple pricing scenarios to maximize national economic benefits over a 30-year period (2006-2035). A particle swarm optimization (PSO) algorithm is implemented to determine the optimal oil production pathway from Ahvaz Oil Field. Subsequently, the study evaluates gas injection prioritization schemes to maximize national interests. The PSO algorithm establishes the following priority ranking for gas allocation Primary priority, Gas exports (including value-added conversion) Secondary priority,Reservoir injection for EOR purposes,Tertiary priority, Direct supply to residential/commercial sectors,Quaternary priority: Other industrial applications. While confirming the strategic importance of gas injection for reservoir management, this research simultaneously emphasizes the necessity for comprehensive gas allocation planning across petrochemical industries, power plants, transportation sectors, and export markets.
JEL Classification: Q35،C61.
Keywords: Gas production optimization trajectory, Time-dependent optimization framework, Particle Swarm Optimization (PSO) algorithm.
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| Keywords: JEL Classification: Q35, C61.
Gas production optimization trajectory, Time-dependent optimization framework, Particle Swarm Optimization (PSO) algorithm. |
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Full-Text [PDF 1421 kb]
(79 Downloads)
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Type of Study: Thesis(PhD.) |
Subject:
Energy Economic
Received: 2025/02/16 | Accepted: 2026/01/27 | Published: 2026/04/30 | ePublished: 2026/04/30
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