Interpreting Downhole Esp Data for Predicting Production Performance by Use of Inversion-Based Methods in South Europe Field

Eleonora Pignotti, Salvatore Spagnolo, S. Pilone, Gianni Baldassarri, P. Cappuccio, Alberto Valente, P. Greco, Mariangela Gonzalez Zamora
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Abstract

The objective of this paper is to demonstrate how a physics-based data driven model and inversion procedures can transform traditional ESP well monitoring into an indispensable tool for predicting multiphase flow rates in ESP production wells. Model and prediction techniques are evaluated by comparison with real field data, measured both live and retroactively from different ESP producing wells located in the South Europe producing field. Operational data commonly gathered by ESP gauge, such as Pressures data, Motor Current and Operative Frequency can be used to predict flow through ESP components, without need for rental of expensive Well Testing equipment. The exploitation of a similar advantage is made possible by the application of artificial intelligence algorithm joined with physics based modelling, taking in as input ESP dynamic data and giving as output a simulation–with acceptable accuracy- of the continuous downhole flow and reservoir properties, allowing the oil operator to obtain key information to optimize well production based on the calculation of ESP operational point. Such cost-effective metering technology is already suitable for online real-time systems implementation and has already been put in place in South Europe field, where it gives reliable results that will yield ongoing ESP run life improvement through its constant application. The improvement of several ESP KPIs, such as MTBF and MTTF, is strictly related to a more accurate follow up of the ESP operative point, hence of the ESP production. Higher ESP MTBF/MTTF might lead to a reduction of the number of necessary ESP replacement workover for year, thus causing the enhancement of hydrocarbon recovery and a reduction of the differed production.In addition to all of this, the possibility of virtual metering well production performance by means of a virtual model might provide a sensible reduction of the number of replacement systems provided from Service Companies, hence the overall optimization of production operation costs. The increasing need for operational efficiency, cost reduction and improved equipment means that service life has driven the recent technological developments related to electrical submersible pump (ESP) well operation management. This paper well described the application and the benefits of such technology to be used as reference successful case by other key players in the O&G market.
利用反演方法解释井下电潜泵数据预测南欧油田生产动态
本文的目的是演示基于物理的数据驱动模型和反演程序如何将传统的ESP井监测转变为预测ESP生产井多相流速率的不可或缺的工具。模型和预测技术通过与南欧生产油田不同ESP生产井的现场和回溯数据进行对比来评估。通常由ESP仪表收集的操作数据,如压力数据、马达电流和工作频率,可用于预测ESP组件的流量,而无需租用昂贵的试井设备。通过将人工智能算法与基于物理的建模相结合,将ESP动态数据作为输入,并以可接受的精度模拟输出连续的井下流动和油藏性质,从而使石油公司能够获得关键信息,从而根据ESP操作点的计算优化油井生产,从而实现类似的优势。这种具有成本效益的计量技术已经适用于在线实时系统实施,并已在南欧油田投入使用,在那里,它提供了可靠的结果,通过不断的应用,可以持续提高ESP的运行寿命。几个ESP kpi的改善,如MTBF和MTTF,与更准确的ESP操作点跟踪密切相关,从而与ESP生产密切相关。更高的ESP MTBF/MTTF可能会减少一年内更换ESP修井的次数,从而提高油气采收率,降低产量差异。除此之外,通过虚拟模型实现虚拟计量井生产性能的可能性,可以显著减少服务公司提供的更换系统的数量,从而实现生产运营成本的整体优化。对作业效率、降低成本和改进设备的需求日益增长,这意味着使用寿命的延长推动了电潜泵(ESP)井作业管理相关技术的发展。本文详细介绍了该技术的应用及所带来的效益,为油气市场上的其他主要参与者提供了成功的参考案例。
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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