Controlling Gas Channeling and Altering It to a kind of Natural Gas Lift in an Iranian Offshore Oil Field

2008 ◽  
Author(s):  
Mohsen Dabiri Nezhad

In an oil field, the optimization study of Continuous Gas Lift (CGL) wells require to encompass the single or multiphase inflow performance within the reservoir and the outflow performance from perforations up to the first separator; along with evaluation of the effect of lift gas injection through the operating Gas Lift Valve (GLV). Mature oil fields that inherit problems of increasing trend of water cut, decreasing trend of Formation Gas Oil Ratio, both leads to further increase in demand for lift gas. To optimize a group of CGL wells sharing common manifold and pipeline, it is required to first optimize every individual well. Well problem and Instability in one well can adversely affect the well performance of other wells of the same gathering system. Literature survey shows that the CGL optimization issues were studied either focused on part of the problem or based on secondary data, and not specific to the challenges of Mature Oil Field. Hence, a study has been carried out to address the well optimization problems with CGL, specifically based on field parameters monitored in a mature offshore oil field. In this present study, development of well models of 12 CGL wells of the study area Heera Field of western Indian offshore basin were carried out, in three different simulators with same set of input data. The best fit well models were selected based on comparative error analysis for approximation of operating points by plotting inflow and outflow performance curves using Nodal Analysis approach. Production History of each well, Flowing Gradient Survey data were correlated with the real-time monitored surface measured field parameters. Deviations were analyzed based on empirical data and screening criteria were used to preliminary diagnose well problems. Simulation experiments were performed on the best fit well models for the detail diagnostic analysis. Recommendations were drawn well wise for optimization, by applying engineering judgments, correlating the simulation results with the surface parameters. Gas Lift Performance Curves (GLPC) were generated for each well by simulation experiment on well models. Non-linear functions of Production rate as a function of Gas Lift Injection Rates (GLIR) were approximated by curve fitting of GLPCs using non-linear regression. GLPC based Lift gas re-allocation problem was formulated and solved with limited lift gas quantity as constraints by Equal Slope Graphical Method and by FMINCON solver of MATLAB. Comparative results of envisaged production gain from both approach is being discussed. The integrated approach for real time monitoring of surface parameters, well model based diagnostic analysis and then further allocation of optimum GLIR to each well shows better results than addressing part of the problem for optimization of CGL wells. Lessons learnt from this ‘integrated optimization approach’ were presented, which will be useful in optimization of CGL wells as standalone well or as a part of a group of wells, especially with similar constraints of mature oil field..


2021 ◽  
Vol 160 ◽  
pp. 105215
Author(s):  
Araceli de Sousa Pires ◽  
Graciela Maria Dias ◽  
Danielly Chagas de Oliveira Mariano ◽  
Rubens Nobumoto Akamine ◽  
Ana Carla Cruz de Albuquerque ◽  
...  

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.


2021 ◽  
Author(s):  
Babalola Daramola

Abstract This publication presents how an oil asset unlocked idle production after numerous production upsets and a gas hydrate blockage. It also uses economics to justify facilities enhancement projects for flow assurance. Field F is an offshore oil field with eight subsea wells tied back to a third party FPSO vessel. Field F was shut down for turnaround maintenance in 2015. After the field was brought back online, one of the production wells (F5) failed to flow. An evaluation of the reservoir, well, and facilities data suggested that there was a gas hydrate blockage in the subsea pipeline between the well head and the FPSO vessel. A subsea intervention vessel was then hired to execute a pipeline clean-out operation, which removed the gas hydrate, and restored F5 well oil production. To minimise oil production losses due to flow assurance issues, the asset team evaluated the viability of installing a test pipeline and a second methanol umbilical as facilities enhancement projects. The pipeline clean-out operation delivered 5400 barrels of oil per day production to the asset. The feasibility study suggested that installing a second methanol umbilical and a test pipeline are economically attractive. It is recommended that the new methanol umbilical is installed to guarantee oil flow from F5 and future infill production wells. The test pipeline can be used to clean up new wells, to induce low pressure wells, and for well testing, well sampling, water salinity evaluation, tracer evaluation, and production optimisation. This paper presents production upset diagnosis and remediation steps actioned in a producing oil field, and aids the justification of methanol umbilical capacity upgrade and test pipeline installations as facilities enhancement projects. It also indicates that gas hydrate blockage can be prevented by providing adequate methanol umbilical capacity for timely dosing of oil production wells.


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.


2013 ◽  
Author(s):  
Xiaodong Liang ◽  
John Stevens ◽  
Dwayne Kelly
Keyword(s):  

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