遥感泄漏检测与量化

L. Amin
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引用次数: 0

摘要

海上石油和天然气设施(就其性质而言)位于偏远地区,既困难又昂贵。尽管存在这些挑战,但与此类资产相关的操作和维护要求是一致的,必须加以解决,这就要求作业者确定最有效的服务形式,以降低人员配备水平、风险和成本。海上油气生产资产通常包含可能导致大量(逸散)气体排放的设备和工艺。其后果是经济和社会(环境)性质的,要求作业者进行排放调查,目的是在尽可能短的时间内识别泄漏并进行补救。这种活动的频率自然是有限的,必须与更广泛的设施的人员配置和操作需求相平衡,这反过来可能导致泄漏检测的次优,以确定时间和可靠性。为了解决访问效率、检测可靠性和结果量化这三个关键挑战,Worley开发了一个遥感平台,该平台结合了无人机(UAV)和现场监测等生产性远程访问设备的使用,以及基于机器的排放检测和算法量化,提供了一个解决方案,使运营商能够增加调查频率,以更低的成本获得更可靠的结果。并以符合安全和低风险操作的方式执行工作。在测试和现场部署中,结果都大大减少了假阳性和阴性,并产生了数据集,可以通过比较泄漏修复活动前后的体积排放来准确指示温室气体减少情况。该技术在很大程度上是数学化的,利用机器学习的编码例程在(最初)有监督的建模条件下执行气体检测,并利用算法气体分散模型进一步量化排放。调查的执行通常通过集成现有的、经过验证的制造传感设备来完成,这些设备跨越几种类型的无人机或现场监测器,这些监测器收集现场数据并传输到基于云的门户网站,该门户网站进一步处理结果。事实证明,该方法在进入难以到达或成本高昂的区域时是有效的,提高了勘探生产率,同时数据处理和量化使作业者能够从改进的可测量性中受益,并相应地优先考虑泄漏修复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Remote Sensing for Leak Detection and Quantification
Offshore oil and gas installations are (by their nature) located in remote locations that are both difficult and costly to access. While such challenges exist, the operate & maintain requirements associated with such assets are consistent and must be addressed, requiring operators to identify the most efficient form of service to reduce staffing levels, risk and cost. Offshore hydrocarbon production assets commonly incorporate equipment and processes that can lead to significant (fugitive) gas emissions. The consequences are both economic and social (environmental) in nature, requiring operators to perform emissions surveys with the objective of leak identification and remediation within the shortest possible timeframe. The frequency of this activity is naturally limited and must be balanced with the staffing and operating needs of the broader facility, which in-turn can lead to sub-optimal leak detection to fix timing and reliability. Addressing the three key challenges of access productivity, detection reliability and results quantification, Worley has developed a remote sensing platform that incorporates the use of productive remote access equipment such as unmanned aerial vehicles (UAV) and in-situ monitoring, with machine based emissions detection and algorithmic quantification to provide a solution that allows the operator to increase survey frequency, obtain more reliable results at lower cost, and perform the work in a manner consistent with safe and low-risk operations. In both testing and field deployments, the results have provided for significant reductions in both false positive and negatives and have produced datasets that allow for accurate indications of greenhouse gas reduction via comparison of volumetric emissions before and after leak repair activity has taken place. The technology is largely mathematical, utilizing coded routines for machine learning to perform gas detection under (initially) supervised modeling conditions, and algorithmic gas dispersion models for further emission quantification. The performance of the survey is typically carried out through the integration of existing, proven manufactured sensing equipment across several types of UAV or in-situ monitors which collect field data for transmission to a cloud-based portal which further processes the results. The approach has been shown effective in accessing hard or costly to reach areas, improving survey productivities, while the data processing and quantification allows the operator to benefit from improved measurability and prioritize leak repair accordingly.
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