Addressing the Limitations of Oil and Gas 4.0 Surrounding Distributed Fiber Optic Data Streams

James Ramsay, L. Noble, Glynn Lockyer, Mohand Alyan, A. Al Shmakhy
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引用次数: 2

Abstract

This paper outlines how the problem of previously unmanageable data volumes produced by distributed fiber optic well monitoring systems is solved through the use of the latest sensing and analytics platform. The platform significantly reduces fiber optic data volumes enabling data to be streamed, processed, stored and visualized; all in real-time. The platform was effectively utilized for real-time data processing and visualization of well injection profiles of fields in the Middle East. The platform addresses the big data challenge associated with streaming distributed fiber optic data in three key areas: Edge processing reduces Distributed Fiber Optic (DFO) data rates by orders of magnitude so it can be streamed from the edge to the end user in real-time.Tiled data storage utilizes innovative data storage strategy to enable fast query responses whether visualizing years or just seconds of DFO data.Elastic infrastructure of processing and storage enables the platform to seamlessly scale and handle variable data rates. Raw Distributed Acoustic Sensing (DAS) data can be generated at rates of 100 MBs per second and cannot feasibly be transferred over a standard internet connection. The sensing and analytics platform's algorithms extract features at the edge which reduce data rates by three orders of magnitude whilst still preserving all key information from the data. Processed DFO data is aggregated and tiled in real-time at tens of different resolutions with respect to both time and fiber length. This enables sub-second query response times even when requesting DFO data across years of historical data. All platform processing logic is designed to run asynchronously on serverless infrastructure. This enables the platform's infrastructure to rapidly scale up or down in response to variable data rates. The result is a cloud-based visualization dashboard capable of displaying DFO data in near real-time across any time range and fiber length. Use of this sensing and analytics platform allowed for seamless streaming of fiber optic data on the Middle East field for injection monitoring, allowing the operator to visualize injection profiles and optimize the injection program in real-time. This sensing and analytics fiber management platform enables the user to highly successfully stream and visualize DFO data in real-time. It enables visibility into the subsurface for production and injection wells, enabling field-wide efficiencies and optimization.
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解决石油和天然气4.0围绕分布式光纤数据流的限制
本文概述了如何通过使用最新的传感和分析平台来解决分布式光纤井监测系统产生的难以管理的数据量问题。该平台显著减少了光纤数据量,使数据能够流化、处理、存储和可视化;所有这些都是实时的。该平台被有效地用于中东油田注水井剖面的实时数据处理和可视化。该平台在三个关键领域解决了与分布式光纤数据流相关的大数据挑战:边缘处理将分布式光纤(DFO)数据速率降低了几个数量级,从而可以将数据从边缘实时传输到最终用户。平铺数据存储利用创新的数据存储策略来实现快速查询响应,无论是可视化数年还是数秒的DFO数据。处理和存储的弹性基础设施使平台能够无缝扩展和处理可变数据速率。原始分布式声学传感(DAS)数据可以以每秒100 mb的速率生成,并且无法通过标准的互联网连接传输。传感和分析平台的算法在边缘提取特征,将数据速率降低三个数量级,同时仍然保留数据中的所有关键信息。处理后的DFO数据根据时间和光纤长度以数十种不同的分辨率实时聚合和平铺。这样即使在请求多年历史数据的DFO数据时,也可以实现亚秒级的查询响应时间。所有平台处理逻辑都设计为在无服务器基础设施上异步运行。这使得平台的基础设施能够快速扩展或缩小,以响应可变的数据速率。其结果是一个基于云的可视化仪表板,能够在任何时间范围和光纤长度内近乎实时地显示DFO数据。使用该传感和分析平台,可以在中东油田实现光纤数据的无缝流,用于注入监测,使作业者能够可视化注入剖面并实时优化注入计划。这种传感和分析光纤管理平台使用户能够非常成功地实时流式传输和可视化DFO数据。它可以实现生产井和注水井的地下可视性,从而实现全油田的效率和优化。
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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