井口实时沥青质顺磁传感的解释挑战与解决方案

John Lovell, Dalia Abdallah, R. Fonseca, M. Grutters, Sameer Punnapala, Omar Kulbrandstad, D. Meza, Jorge Baez
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摘要

沥青质沉积为中东及其他地区的石油生产提供了重要的流动保证。直到最近,还没有一种无需干预的方法来监测受沥青质影响的井的沉积情况。这促使ADNOC赞助MicroSilicon开发一种无需干预的实时传感器设备,以监测沥青质沉积。这种最新的先进设备目前安装在ADNOC运营油田的井口和附近设施,并自动收集数据。历史上测量石油中沥青质的方法依赖于实验室过程,使用溶剂和重量技术相结合的方法提取沥青质。顺磁技术提供了一种可能更简单的替代方法,因为已知每克油的自旋是该油的恒定特性,至少当油处于恒定的温度和压力下时是这样。将设备带到现场意味着任何解释都需要独立于这些属性。此外,进入传感器的流体是多相的,并且受到不同温度和压力的影响,这给将原始光谱数据转换为沥青质数量和粒度带来了挑战。这些挑战是通过硬件、软件和基于云的机器学习技术的结合来解决的。对20多口井的石油进行了实时采样,并确认沥青质百分比不仅随井而异,而且是生产的动态方面,一些井的水平相对恒定,而另一些井则呈现出持续的变化。对另一口井进行了连续观察,发现在地面发生节流后,沥青质水平有所下降。通过机器学习增强的诊断数据补充了沥青质的测量,并提供了比以前更完整的流动保证挑战的图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpretation Challenges and Solutions for Real-Time Asphaltene Paramagnetic Sensing at the Wellhead
Asphaltene deposition presents a significant flow assurance to oil production in many parts of the Middle East and beyond. Until recently, there had been no intervention-free approach to monitor deposition in the asphaltene affected wells. This prompted ADNOC to sponsor MicroSilicon to develop of an intervention less real-time sensor device to monitor asphaltene deposition. This new state-of-the-art device is currently installed and automatically collecting data at the wellhead and nearby facilities of an ADNOC operated field. Historic ways of measuring asphaltene in oil relied upon laboratory processes that extracted the asphaltene using a combination of solvents and gravimetric techniques. Paramagnetic techniques offer a potentially simpler alternative because it is known that the spins per gram of an oil is a constant property of that oil, at least when the oil is at constant temperature and pressure. Taking the device to the field means that any interpretation needs to be made independent of these properties. Additionally, the fluid entering the sensor is multiphase and subject to varying temperature and pressure which raises challenges for the conversion of raw spectroscopic data into asphaltene quantity and particle size. These challenges were addressed with a combination of hardware, software and cloud-based machine learning technologies. Oil from over two dozen wells has been sampled in real-time and confirmed that the asphaltene percentage does not just vary from well to well but is also a dynamic aspect of production, with some wells having relatively constant levels and others showing consistent variation. One other well was placed on continuous observation and showed a decrease in asphaltene level following a choke change at the surface. Diagnostic data enhanced by machine learning complements the asphaltene measurement and provides a much more complete picture of the flow assurance challenge than had been previously been available.
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