ESP Performance Monitoring and Diagnostics for Production Optimization in Polymer Flooding: A Case Study of Mangala Field

Nitesh Agrawal, T. Chapman, Rahul Baid, Ritesh K. Singh, S. Shrivastava, M. Kushwaha, Jayabrata Kolay, P. Ghosh, Joyjit Das, S. Khare, Piyush Kumar, S. Aggarwal
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引用次数: 2

Abstract

The objective of this paper is to present a suite of diagnostic methods and tools which have been developed to analyse and understand production performance degredation in wells lifted by ESPs in the Mangala field in Rajasthan, India. The Mangala field is one of the world’s largest full field polymer floods, currently injecting some 450kbbl/day of polymerized water, and a significant proportion of production is lifted with ESPs. With polymer breaking through to the producers, productivity and ESP performance in many wells have changed dramatically. We have observed rapidly reducing well productivity indexes (PI), changes to the pumps head/rate curve, increased inlet gas volume fraction (GVF) and reduction in the cooling efficiency of ESP motors from wellbore fluids. The main drivers for the work were to understand whether reduced well rates were a result of reduced PI or a degredation in the ESP pump curve, and whether these are purely down to polymer or combined with other factors, for example reduced reservoir pressure, increasing inlet gas, scale buildup, mechanical wear or pump recirculation. The methodology adopted for diagnosis was broken in 5 parts – 1) Real time ESP parameter alarm system, 2) Time lapse analysis of production tubing pressure drop, 3) Time lapse analysis of pump head de-rating factor, 4) Time lapse analysis of pump and VFD horse power 5) Dead head and multi choke test data. With this workflow we were able to break down our understanding of production loss into its constituent components, namely well productivitiy, pump head/rate loss or additional tubing pressure drop. It was also possible to further make a data driven asseesment as to the most likely mechanisms leading to ESP head loss (and therefore rate loss), to be further broken own into whether this was due to polymer plugging, mechanical wear, gas volume fraction (GVF) de-rating, partial broken shaft/locked diffusers or holes/recirculation. In some cases a specific mechanism was compounded with an associated impact. For example, in ESPs equipped with an inlet screen, heavy polymer deposition over the screen was resulting in large pressure drops across the screen leading to lower head, but this also resulted in higher GVFs into first few stages of the pump, even though the GVF outside the pump were low, leading to further head loss from gas de-rating of the head curve. With knowledge of the magnitude of production losses from each of the underlying mechanisms, targeted remediation could then be planned. The well and pump modelling adopted in the workflow utilise standard industry calculations, but the combination of these into highly integrated visual displays combined with time lapse analysis of operating performance, provide a unique solution not seen in commercial software we have screened. The paper also provides various real field examples of ESP performance deterioration, showing the impact of polymer deposition leading to increased pump hydraulic friction losses, pump mechanical failure and high motor winding temperature. Diagnoses based on the presented workflow have in many cases been verified by inspection reports on failed ESPs. Diagnosis on ESPs that have not failed cannot be definitive, though the results of remediation (eg pump flush) can help to firm up the probable cause.
聚合物驱ESP性能监测与诊断——以Mangala油田为例
本文的目的是介绍一套诊断方法和工具,用于分析和了解印度拉贾斯坦邦Mangala油田esp提升井的生产性能退化。Mangala油田是世界上最大的全油田聚合物驱之一,目前每天注入约450万桶聚合水,其中很大一部分产量是通过esp提高的。随着聚合物进入生产商,许多井的产能和ESP性能发生了巨大变化。我们观察到油井产能指数(PI)迅速下降,泵扬程/速率曲线发生变化,进口气体体积分数(GVF)增加,井筒流体对ESP马达的冷却效率降低。这项工作的主要驱动因素是了解井速降低是由于PI降低还是ESP泵曲线的退化,以及这些是纯粹由于聚合物还是与其他因素相结合,例如储层压力降低、进口气体增加、结垢、机械磨损或泵再循环。诊断方法分为5部分:1)ESP参数实时报警系统,2)生产油管压降时移分析,3)泵扬程降级因子时移分析,4)泵和变频器马力时移分析,5)死头和多节流阀试验数据。通过这种工作流程,我们能够将生产损失分解为其组成部分,即油井产能、泵扬程/速率损失或额外的油管压降。还可以进一步进行数据驱动评估,以确定导致ESP扬程损失(从而导致速率损失)的最可能机制,并进一步细分为聚合物堵塞、机械磨损、气体体积分数(GVF)降低、轴部分断裂/扩散器锁定或井眼/再循环。在某些情况下,具体的机制与相关的影响相结合。例如,在配备进口筛管的esp中,筛管上的大量聚合物沉积会导致筛管两端的压降很大,导致扬程降低,但这也会导致泵前几级的GVF升高,即使泵外的GVF很低,也会导致扬程曲线的气体降级导致进一步的扬程损失。了解了每种潜在机制造成的生产损失程度后,就可以计划有针对性的补救措施。工作流程中采用的油井和泵模型利用标准的行业计算,但将这些模型结合成高度集成的视觉显示,并结合作业性能的延时分析,提供了我们所筛选的商业软件中未见的独特解决方案。文中还提供了各种ESP性能下降的现场实例,说明聚合物沉积导致泵的水力摩擦损失增加、泵的机械故障和电机绕组温度升高的影响。在许多情况下,基于所提出的工作流程的诊断已经通过对故障esp的检查报告进行了验证。虽然修复的结果(如泵冲洗)可以帮助确定可能的原因,但对未失效的ESPs的诊断不能确定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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