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Monitoring Production Well Testing Facilities Using Statistical Analysis

机译：使用统计分析监测生产井测试设施

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There are over 50 well test facilities operated by BPXA on the North Slope of Alaska. The test facilities provide production measurement for 100s of producing wells that produce 100,000s of barrels per day. Well tests are a critical surveillance method for production optimization and reservoir performance monitoring of oil and gas fields. The quality of well tests directly impacts production operations, allocation, well workover activities, and capital investment decisions. A cost efficient method to determine the quality of well testing operations is to use well test repeatability statistics. Observing well test data over periods of time provides the ability to identify anomalies in well operations and metering performance. This paper will discuss the details of an algorithm that was developed to determine the repeatability of well testing for all BP-operated North Slope well test facilities. This algorithm identifies and filters outlier well tests due to operational changes. Remaining well tests are then treated statistically to determine measurement performance. A web application was developed to allow viewing, sorting, and analysis of well test repeatability statistical data by operational personnel. This application is used to rank well test facilities and wells with high deviations in gross fluid, gas rates, watercut, and oil rates. Examples of the application with actual field data are provided in the paper. Access to this data allows for improved business decision making, identification of problematic well test facilities, and prioritization of repairs and upgrades to equipment.
机译：在阿拉斯加北坡上有50多个井测试设施。测试设施为100多人生产生产的生产测量，每天生产100,000桶。井测试是石油和天然气田的生产优化和水库性能监测的关键监测方法。井测试的质量直接影响生产运营，分配，工作台活动和资本投资决策。确定井测试操作质量的成本有效的方法是使用井测试重复性统计数据。在时间段内观察到井测试数据提供了识别井作业和计量性能的异常的能力。本文将讨论开发的算法的细节，以确定所有BP操作北坡井测试设施的良好测试的可重复性。该算法识别并因操作变化而筛选出异常测试。然后统计治疗剩余的井测试以确定测量性能。开发了一种Web应用程序，以允许通过运营人员查看，分类和分析井测试重复性统计数据。本申请用于排名良好的测试设施和井中的良好测试设施和井中的液体，气体速率，水围和溢油率高。本文提供了具有实际现场数据的应用的示例。访问此数据允许改进的业务决策，识别有问题的井测试设施，以及维修和升级到设备的优先排序。

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