揭示数据的价值——一个用于日志操作的大规模端到端数据分析系统

V. Torlov, M. Kanfar, Sachit Saumya, Oscar Gill
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引用次数: 1

摘要

本文介绍了电缆测井作业分析系统和专利工作流程的总结,该系统为测井作业评估带来了范式转变。其目的是全面了解测井作业,优化时间、作业能力、资源,减少碳足迹。所描述的数字化转型计划导致了大规模端到端数据分析系统的设计和实现。该过程始于2020年,当时使用了事件跟踪软件,使测井工程师能够创建一个全面的数据集,其中包含详细的标记电缆测井活动记录和大量的作业元数据。这些记录包括时间深度、地面和井下张力数据、钻井参数日志、详细作业参数、井下仪器和时间图活动,构成了一个全面的分层事件树。在作业期间跟踪操作事件,或者根据原始日志签名堆栈标识操作事件,并通过使用专用接口的交互式图形处理捕获操作事件。为了利用大数据,设计了一系列沉浸式分析仪表板来解决不同各方的查询。该系统集成了来自多个来源的数据,并包括广泛的ETL(提取、转换和加载)例程。通过多次查询和进一步计算,揭示了测井领域数据的全部价值,包括作业效率、事故记录、井况跟踪、可持续性等。领域知识与统计和机器学习分析相结合,可以对性能影响因素进行排名,并建立可变的操作性能指标。使用已建立的基线,将活动持续时间与计算出的参考值进行比较,并根据感兴趣的属性(例如,测井公司或工作人员、区域、钻机、运输工具、类别或不同的活动、作业类型、时间等)进行总结。定期总结审查允许通过各种参数跟踪测井公司在既定指标方面的表现。一些测井公司已经引入了Engineer的记分卡,该记分卡为能力评估提供了性能洞察和输入。实现的方法远离了基本的效率评估(二元生产时间和非生产时间)。该系统是大规模测井作业数据分析驱动解决方案的先驱。根据测井公司的说法,这是世界上第一个详尽的测井活动记录,可用于评估作业绩效,识别,规定和跟踪改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling the Value of Data - An End-to-End Data Analytics System at Scale for Logging Operations
This paper presents a summary of a Wireline Logging Operations Analytics System and a patented workflow that has brought a paradigm shift to a logging operations assessment. The aim was to provide a holistic insight into logging operations and enable optimization of time, operational capability, resources, and reduction in carbon footprint. The described digital transformation initiative led to the design and implementation of an end-to-end data analytics system at scale. The process started in 2020 with the implementation of events tracking software that enabled Logging Engineers to create a comprehensive dataset containing detailed labelled wireline logging activities record with an extensive set of job metadata. The records comprise temporal depth, surface, and downhole tension data, drilling parameters log, detailed job parameters, downhole instrumentation, and time-mapped activities which constitutes a comprehensive hierarchical events tree. The operational events are either tracked during the job or identified based on a stack of raw log signatures and are captured through an interactive graphical process using a dedicated interface. To leverage the big data, sets of immersive analytics dashboards was designed to address different parties’ inquiries. The system integrates data from multiple sources and includes extensive ETL (extract, transform & load) routines. Multiple queries and further computations were applied to unveil the full value of data within the Well Logging domain including operating efficiency, incidents records, hole conditions tracking, sustainability etc. Domain knowledge paired with statistical and machine learning analyses allowed ranking performance affecting factors and establishing the variable operational performance metrics. Using the established baselines, activities duration is compared to a calculated reference, and is summarized over the attribute of interest, e.g. logging company or a crew, area, rig, conveyance, category or distinct activity, job type, time etc. Periodic summary review allows tracking the performance of Logging company in respect to established metrics by a variety of parameters. Some Logging companies have introduced Engineer's scorecard that provides performance insights and input to a competency assessment. The implemented approach moves away from a rudimentary efficiency assessment (binary productive vs non-productive time). The implemented system is a pioneer in data analytics driven solution at scale for logging operations. According to the Logging companies, it is the first exhaustive recording of logging activities in the world, that can be used to evaluate operational performance, identify, prescribe, and track the improvements.
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