Production Data Management Collaboration Effort in an Integrated Journey for More than 1,000 Wells in the Northern Kuwait Heavy Oil Fields

S. González, Tamadhor Al Muhanna, Waeil Abdelmohen Abdalla, D. C. Pandey, Ahmad Naqi, Loloh Al Mezal, Aisha Al Saqer, S. Rajab, A. Safar, Greg Gonzalez, Satinder Malik, I. Fadul, A. Hamlaoui
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引用次数: 1

Abstract

Based on the North Kuwait Heavy Oil fields’ development plan, by the end of 2019 more than 1,000 wells will be connected to the producing facilities. An extensive amount of surface and subsurface data will be collected and transmitted to the central databases. This paper describes the Data Management processes and workflows currently in place not only to use the captured and analyzed data for production and facilities optimized and safe operation but also the strategic plan for future integrated wells and facilities management. Different approaches for both at wells and surface facilities data sets are being implemented not only to monitor and optimize the wells and field performance but also to provide other disciplines with the right data in the right format at the right time. The vision is to move away from the current approach to a new one to handle automated real-time data capture, data analysis, data visualization and Exception Based surveillance within the domain of CWE (Collaborative Work Environment). A single data repository has been used to ensure seamless communication from the field facilities and wells directly to the end-user workstations. Data algorithms are run in daily basis to detect anomalies in millions of data point of parameters allowing either proactive interventions or understanding the reasons for deviation from normal expected operating parameters. By implementing a daily surveillance routine and simple exception-based monitoring rules along with advanced data algorithms on wells (including artificial lift system) and facilities parameters, it was possible to detect wells without production, production recirculation due to holes in the tubing, flowlines plugging and downhole sand issues. The importance of Data Management falls into predictive analysis techniques focused on increasing the uptime of wells and facilities, supported by typical data science algorithms such as clustering, advance filtering, detection of data anomalies, regression and classification. This paper will also discuss how a holistic approach has evolved in managing the current operations, capture the lessons learned for not only optimizing the current field operation but also use the knowledge gained for future development strategy; the result: an approach to a collaborative environment to help the team to analyze performances, make decisions and create strategies to increase production, reduce lead time and reduce costs. The Production Data Management strategies implemented in the early stages of the project have already generated significant value in picking up and prioritizing wells with issues, detection of flowline plugging, and Artificial lift system issues resulting not only in maintaining the production plateau but also reducing operating expenses while improving the restoration time.
在科威特北部重油油田1000多口井的综合作业中,生产数据管理协作工作
根据北科威特重油油田的开发计划,到2019年底,将有1000多口井与生产设施相连。大量的地表和地下数据将被收集并传输到中央数据库。本文介绍了目前的数据管理流程和工作流程,不仅可以使用捕获和分析的数据来优化生产和设施的安全运行,还可以为未来的综合井和设施管理制定战略计划。针对井内和地面设施的数据集,采用了不同的方法,不仅可以监测和优化井和现场的性能,还可以在正确的时间以正确的格式为其他学科提供正确的数据。其愿景是从当前的方法转向一种新的方法,以处理CWE(协同工作环境)领域内的自动实时数据捕获、数据分析、数据可视化和基于异常的监视。单一数据存储库用于确保从现场设施和井直接到最终用户工作站的无缝通信。数据算法每天运行,以检测数百万个数据点参数中的异常情况,从而可以进行主动干预或了解偏离正常预期操作参数的原因。通过对井(包括人工举升系统)和设施参数实施日常监测和简单的例外监测规则,以及先进的数据算法,可以检测到由于油管孔、管线堵塞和井下砂问题而没有生产、生产再循环的井。数据管理的重要性在于预测分析技术,其重点是增加油井和设施的正常运行时间,并辅以典型的数据科学算法,如聚类、预先过滤、数据异常检测、回归和分类。本文还将讨论如何采用整体方法来管理当前的作业,总结经验教训,不仅可以优化当前的现场作业,还可以将所获得的知识用于未来的发展战略;其结果是:一种协作环境的方法,可以帮助团队分析性能、做出决策并制定策略,以提高产量、缩短交货时间和降低成本。在项目的早期阶段实施的生产数据管理策略已经产生了巨大的价值,在发现和优先处理有问题的井、检测流线堵塞和人工举升系统问题方面,不仅保持了生产平台,还降低了运营成本,同时缩短了恢复时间。
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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