12 Months of Real-Time Digital Chemistry in 3-Phase Flow - Lessons Learned and Plans Forward

John Lovell, Omar Kulbrandstad, Sai Madem, M. Godoy
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Abstract

By miniaturizing and ruggedizing equipment used for quantum paramagnetic spectroscopy, it is now possible to take a real-time chemical snapshot of molecules flowing through the wellhead or other surface fixtures. The digital time-series captures unique chemical properties of the fluid, such as the percentage of asphaltene in the oil, the oil-water ratio and gas-oil ratio. That data can be transmitted via industry-standard cloud protocols and be monitored from a global service center. 12 months of real-time data has been collected from operations around the world and the real-time monitoring has enabled prompt feedback for upgrades in both hardware and software. In a three-phase well configuration that had high rates of both water (over 90%) and gas (~1 MMSCf/day), this feedback drove some significant hardware modifications in order to optimize the consistency of asphaltene data. The heart of the system is a microwave resonator that was designed to receive fluid at wellhead conditions with minimal reduction from wellhead pressure and temperature. The parameters of the resonator were optimized to maximize microwave intensity for typical oilfield fluids. A tailor-made set-up of fluid accumulator and control-valves upstream of the resonator ensured that the resonator could obtain samples that were mostly oil. By combining the resonator with a solenoid that created a large magnetic field across the oil, the resulting system provided spectroscopic data similar to that available in chemical laboratories but in a smaller package and one that tolerates some gas and conductive water in the oil. The combined quantum data is now provided continuously to the operator via a cloud or other communication architecture of operator choosing. It is anticipated that the resulting Internet of Things (IoT) system will make possible the optimization of chemical program and asphaltene remediation by incorporating system data with integrated flow assurance management. Qualification for offshore is ongoing with 5ksi pressure certification already achieved. It was not obvious before installation, but once the 3-phase system was installed and the data transmitting in real-time, it became clear that software to automatically extract asphaltene information from spectral data needed to be able to cope with sudden and large changes in both asphaltene level and water-cut/gas-oil ratio which in turn required building an adaptive software model. Asphaltene percentage at one producing well was seen to vary from 0.3% to 3% in a single day. It was also discovered from the cloud-based monitoring that daily temperature variation introduced a phase variation in the shape of the sensor response. Correct derivation of spectral voltages was achieved through the combination of machine learning, model-based analysis and additional diagnostic data such as the quality factor of the resonator and its resonance frequency. As a consequence, the AI-based software could extract the not only the asphaltene percentage but the oil-water cut in the resonator and its gas-oil ratio. For the first time, it is now possible to make a change in, say injected chemicals, look at the times-series data for the corresponding change in asphaltene and then adjust the chemicals accordingly. Such frequency of sampling (and volume of data) would be too much to handle with samples collected by hand. This device lays the platform for a multiplicity of chemical sensors to be connected to the cloud in real-time and in turn sets the stage to take the hardware offshore and eventually to subsea.
12个月的实时数字化学在三相流-经验教训和计划向前
通过量子顺磁光谱设备的小型化和加固,现在可以对流过井口或其他地面装置的分子进行实时化学快照。数字时间序列捕捉流体的独特化学性质,如沥青质在油中的百分比、油水比和气油比。这些数据可以通过行业标准的云协议传输,并从全球服务中心进行监控。从世界各地的作业中收集了12个月的实时数据,实时监测能够及时反馈硬件和软件的升级。在三相井配置中,水产率(超过90%)和气产率(约1 MMSCf/天)都很高,为了优化沥青质数据的一致性,这些反馈推动了一些重大的硬件修改。该系统的核心是一个微波谐振器,设计用于在井口条件下接收流体,井口压力和温度的降低最小。对谐振器参数进行了优化,使典型油田流体的微波强度最大化。在谐振器的上游,定制了流体蓄能器和控制阀,确保了谐振器可以获得大部分为油的样品。通过将谐振器与螺线管结合在一起,在油中产生一个大的磁场,最终的系统提供了类似于化学实验室的光谱数据,但封装更小,并且可以容忍油中的一些气体和导电水。组合的量子数据现在通过云或运营商选择的其他通信架构连续地提供给运营商。预计由此产生的物联网(IoT)系统将通过将系统数据与集成的流动保证管理相结合,使化工程序和沥青质修复的优化成为可能。目前已经通过了5ksi压力认证,正在进行海上资质认证。这在安装前并不明显,但一旦安装了三相系统并实时传输数据,就很明显,从光谱数据中自动提取沥青质信息的软件需要能够应对沥青质水平和含水/气油比的突然和巨大变化,这又需要建立一个自适应的软件模型。在一口生产井中,沥青质百分比在一天内变化在0.3%到3%之间。从基于云的监测中还发现,每天的温度变化会导致传感器响应形状的相位变化。通过结合机器学习、基于模型的分析和额外的诊断数据(如谐振器的质量因子及其谐振频率),实现了频谱电压的正确推导。因此,基于人工智能的软件不仅可以提取沥青质百分比,还可以提取谐振器中的油水含水率及其油气比。现在,我们第一次可以改变注入的化学物质,通过观察沥青质的时间序列数据,然后相应地调整化学物质。这样的采样频率(和数据量)对于手工采集的样本来说太大了。该设备为多种化学传感器提供了平台,这些传感器可以实时连接到云端,从而为将硬件带到海上并最终进入海底奠定了基础。
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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