成熟油田气举井管理和优化的成功案例:数字化油田的自动化工作流程加速了机会创造和生产优化

A. Nazri, W. K. Anuar, Lucas Ignatius Avianto Nasution, Hayati Turiman, S. Shafie, Mohamad Mustaqim Mokhlis
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引用次数: 0

摘要

位于马来西亚海上的S油田已经生产了30多年,目前近90%的现役管柱依赖于气举辅助。在这个成熟的油田中,不断增加的含水率、出砂率和储层压力的管理等地下挑战是推动资产团队持续监测气举井性能的关键因素之一,以确保更好地控制产量,从而达到油田的目标产能。因此,在S油田开展了气举优化(GLOP)活动,通过集成集成作业(IO)中的气举管理模块来加速短期生产。从气举健康检查、诊断和故障排除到数据和模型验证,直到在数字工作流程的帮助下识别GLOP候选者之前执行,资产团队创建了一个工作流程来指导该活动。油田S开发的数字油田和集成作业(IO)提供了一个高效的协同工作环境,可以实时监控油田性能并持续优化生产。Digital Fields由多个开发和操作的工程工作流程组成,作为资产团队快速识别容易实现的机会的推动者。本文将重点关注数字工作流的整个周期过程,工程师在数据卫生和模型验证方面的干预,实施GLOP的挑战,以及领域S活动的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Success Story in Managing and Optimising Gas Lift Wells in Matured Oil Field: Automated Workflows in Digital Fields as Enablers to Accelerate Opportunities Creation and Production Optimisation
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.
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