Systems and methods for likelihood-based detection of gas leaks using mobile survey equipment

    公开(公告)号:US09599597B1

    公开(公告)日:2017-03-21

    申请号:US14139348

    申请日:2013-12-23

    Applicant: Picarro Inc.

    CPC classification number: G01N33/0004 G01M3/04 G01N33/0075

    Abstract: In some embodiments, vehicle-based natural gas leak detection methods are used to generate 2-D spatial distributions (heat maps) of gas emission source probabilities and surveyed area locations using measured gas concentrations and associated geospatial (e.g. GPS) locations, wind direction and wind speed, and atmospheric condition data. Bayesian updates are used to incorporate the results of one or more measurement runs into computed spatial distributions. Operating in gas-emission plume space rather than raw concentration data space allows reducing the computational complexity of updating gas emission source probability heat maps. Gas pipeline location data and other external data may be used to determine the heat map data.

    Plume estimation using correlation measurements at isolated spatial points
    5.
    发明授权
    Plume estimation using correlation measurements at isolated spatial points 有权
    在孤立的空间点使用相关测量的羽流估计

    公开(公告)号:US09470517B1

    公开(公告)日:2016-10-18

    申请号:US13866660

    申请日:2013-04-19

    Applicant: Picarro, Inc.

    Inventor: Chris W. Rella

    CPC classification number: G01N33/0062 G01N1/26

    Abstract: Repeated simultaneous concentration measurements at spatially separated points are used to provide information on the lateral spatial extent of a gas plume. More specifically the spatial correlations in this data provide this information. Fitting a gas plume model directly to this multi-point data can provide good estimates of total plume emission. The distance between the plume source and the measurement points does not need to be known to provide these estimates. It is also not necessary to perform any detailed atmospheric modeling.

    Abstract translation: 在空间分离的点上重复的同时浓度测量被用于提供关于气体羽流的横向空间范围的信息。 更具体地说,该数据中的空间相关性提供了该信息。 将气体羽毛模型直接装配到该多点数据可以提供总羽流发射的良好估计。 不需要知道羽流源与测量点之间的距离来提供这些估计值。 也不需要进行任何详细的大气建模。

    Methods for gas leak detection and localization in populated areas using two or more tracer measurements
    6.
    发明申请
    Methods for gas leak detection and localization in populated areas using two or more tracer measurements 审中-公开
    使用两次或多次示踪剂测量的人口稠密地区气体泄漏检测和定位方法

    公开(公告)号:US20160216172A1

    公开(公告)日:2016-07-28

    申请号:US15088885

    申请日:2016-04-01

    Applicant: Picarro, Inc.

    Abstract: Improved gas leak detection from moving platforms is provided. Automatic horizontal spatial scale analysis can be performed in order to distinguish a leak from background levels of the measured gas. Source identification can be provided by using two or more tracer measurements of isotopic ratios and/or chemical tracers to distinguish gas leaks from other sources of the measured gas. Multi-point measurements combined with spatial analysis of the multi-point measurement results can provide leak source distance estimates. Qualitative source identification is provided. These methods can be practiced individually or in any combination.

    Abstract translation: 提供了从移动平台改进的气体泄漏检测。 可以进行自动水平空间尺度分析,以区分泄漏与被测气体的背景水平。 可以通过使用两个或更多个同位素比率和/或化学示踪剂的示踪剂测量来提供源识别,以区分气体泄漏与测量气体的其他来源。 多点测量结合多点测量结果的空间分析可以提供泄漏源距离估计。 提供定性来源识别。 这些方法可以单独或以任何组合实施。

    Ring-down binning in FSR hopping mode
    7.
    发明授权
    Ring-down binning in FSR hopping mode 有权
    FSR跳频模式下的振铃分级

    公开(公告)号:US09267880B1

    公开(公告)日:2016-02-23

    申请号:US14634682

    申请日:2015-02-27

    Applicant: Picarro, Inc.

    CPC classification number: G01N21/39 G01J3/00 G01J3/42 G01J3/427 G01N2021/399

    Abstract: For cavity enhanced optical spectroscopy, the cavity modes are used as a frequency reference. Data analysis methods are employed that assume the data points are at equally spaced frequencies. Parameters of interest such as line width, integrated absorption etc. can be determined from such data without knowledge of the frequencies of any of the data points. Methods for determining the FSR index of each ring-down event are also provided.

    Abstract translation: 对于腔增强光谱,腔模式用作频率参考。 采用数据分析方法,假设数据点处于等间隔的频率。 可以从这样的数据中确定诸如线宽,积分吸收等的关注参数,而不知道任何数据点的频率。 还提供了确定每个减号事件的FSR指数的方法。

    Methods for gas leak detection and localization in populated areas using horizontal analysis
    8.
    发明申请
    Methods for gas leak detection and localization in populated areas using horizontal analysis 有权
    采用横向分析方法对人口稠密地区进行气体泄漏检测和定位

    公开(公告)号:US20140032160A1

    公开(公告)日:2014-01-30

    申请号:US13656080

    申请日:2012-10-19

    Applicant: Picarro, Inc.

    CPC classification number: G01M3/20 G01N21/3504 G01N33/0004 G01N2201/025

    Abstract: Improved gas leak detection from moving platforms is provided. Automatic horizontal spatial scale analysis can be performed in order to distinguish a leak from background levels of the measured gas. Source identification can be provided by using isotopic ratios and/or chemical tracers to distinguish gas leaks from other sources of the measured gas. Multi-point measurements combined with spatial analysis of the multi-point measurement results can provide leak source distance estimates. These methods can be practiced individually or in any combination.

    Abstract translation: 提供了从移动平台改进的气体泄漏检测。 可以进行自动水平空间尺度分析,以区分泄漏与被测气体的背景水平。 可以通过使用同位素比率和/或化学示踪剂来提供源识别,以区分气体泄漏与测量气体的其他来源。 多点测量结合多点测量结果的空间分析可以提供泄漏源距离估计。 这些方法可以单独或以任何组合实施。

    Leak detection event aggregation and ranking systems and methods

    公开(公告)号:US11933774B1

    公开(公告)日:2024-03-19

    申请号:US18060111

    申请日:2022-11-30

    Applicant: Picarro Inc.

    CPC classification number: G01N33/0075 G01M3/04 G01N33/0047

    Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak source. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.

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