Numerical Simulation of Gas Lift Optimization Using Genetic Algorithm for a Middle East Oil Field: Feasibility Study

2020 ◽  
Author(s):  
Mustafa AlJuboori ◽  
Mofazzal Hossain ◽  
Omar Al-Fatlawi ◽  
Akim Kabir ◽  
Abbas Radhi
2021 ◽  
Author(s):  
Mohammed Ahmed Al-Janabi ◽  
Omar F. Al-Fatlawi ◽  
Dhifaf J. Sadiq ◽  
Haider Abdulmuhsin Mahmood ◽  
Mustafa Alaulddin Al-Juboori

Abstract Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorithm to tackle the challenging task of optimally allocating the gas lift injection rate through numerical modeling and simulation studies to maximize the oil production of a Middle Eastern oil field with 20 production wells with limited amount of gas to be injected. The key objective of this study is to assess the performance of the wells of the field after applying gas lift as an artificial lift method and applying the genetic algorithm as an optimization algorithm while comparing the results of the network to the case of artificially lifted wells by utilizing ESP pumps to the network and to have a more accurate view on the practicability of applying the gas lift optimization technique. The comparison is based on different measures and sensitivity studies, reservoir pressure, and water cut sensitivity analysis are applied to allow the assessment of the performance of the wells in the network throughout the life of the field. To have a full and insight view an economic study and comparison was applied in this study to estimate the benefits of applying the gas lift method and the GA optimization technique while comparing the results to the case of the ESP pumps and the case of naturally flowing wells. The gas lift technique proved to have the ability to enhance the production of the oil field and the optimization process showed quite an enhancement in the task of maximizing the oil production rate while using the same amount of gas to be injected in the each well, the sensitivity analysis showed that the gas lift method is comparable to the other artificial lift method and it have an upper hand in handling the reservoir pressure reduction, and economically CAPEX of the gas lift were calculated to be able to assess the time to reach a profitable income by comparing the results of OPEX of gas lift the technique showed a profitable income higher than the cases of naturally flowing wells and the ESP pumps lifted wells. Additionally, the paper illustrated the genetic algorithm (GA) optimization model in a way that allowed it to be followed as a guide for the task of optimizing the gas injection rate for a network with a large number of wells and limited amount of gas to be injected.


Author(s):  
H. Saadawi

Specifying and selecting equipment for gas compression projects is a complex process involving many engineering disciplines. All the alternatives and the possible interaction between the various components in the system should be carefully examined by the project team. The accumulation of errors in evaluating the system characteristics during the project engineering phase, can lead to the gas compression system not performing to design specifications. This paper describes the problems encountered with the compressor package during the commissioning of four gas turbine-driven compressor stations for gas lift in one of the onshore oilfields in the Middle East. Solutions to these problems are also outlined.


2006 ◽  
Author(s):  
Shahab D. Mohaghegh ◽  
Hafez H. Hafez ◽  
Razi Gaskari ◽  
Masoud Haajizadeh ◽  
Maher Kenawy

2021 ◽  
Author(s):  
Amir Badzly M. Nazri ◽  
W. M. Anas W. Khairul Anuar ◽  
Lucas Ignatius Avianto Nasution ◽  
Hayati Turiman ◽  
Shar Kawi Hazim Shafie ◽  
...  

Abstract Field S located in offshore Malaysia had been producing for more than 30 years with nearly 90% of current active strings dependent on gas lift assistance. Subsurface challenges encountered in this matured field such as management of increasing water-cut, sand production, and depleting reservoir pressure are one of key factors that drive the asset team to continuously monitor the performance of gaslifted wells to ensure better control of production thereby meeting target deliverability of the field. Hence, Gas Lift Optimization (GLOP) campaign was embarked in Field S to accelerate short term production with integration of Gas Lift Management Modules in Integrated Operations (IO). A workflow was created to navigate asset team in this campaign from performing gaslift health check, diagnostic and troubleshooting to data and model validation until execution prior to identification of GLOP candidates with facilitation from digital workflows. Digital Fields and Integrated Operations (IO) developed in Field S provided an efficient collaborative working environment to monitor field performance real time and optimize production continuously. Digital Fields comprises of multiple engineering workflows developed and operationalized to act as enablers for the asset team to quickly identify the low-hanging fruit opportunities. This paper will focus on entire cycle process of digital workflows with engineer's intervention in data hygiene and model validation, the challenges to implement GLOP, and results from the campaign in Field S.


2014 ◽  
Author(s):  
Hector Aguilar ◽  
Aref Almarzooqi ◽  
Tarek Mohamed El Sonbaty ◽  
Leigber Villarreal

2016 ◽  
Author(s):  
Mohammad Yunus Khan ◽  
Anupam Tiwari ◽  
Shuichiro Ikeda ◽  
Fahad I. Syed ◽  
Alunood K. Al Sowaidi ◽  
...  

2000 ◽  
Vol 2000 (188) ◽  
pp. 553-558 ◽  
Author(s):  
Yasuhisa Okumoto ◽  
Kouhei Murase ◽  
Asako Tanaka

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