公开(公告)号：US10962438B2

公开(公告)日：2021-03-30

申请号：US16826265

申请日：2020-03-22

Applicant: Picarro Inc.

Inventor： David Steele ,  Chris W. Rella ,  Sze M. Tan ,  Aaron Van Pelt

IPC: G01M3/22 ,  F17D5/02 ,  G01M3/04 ,  G01C21/36 ,  G01P5/02 ,  G01P13/02

Abstract: In some embodiments, a natural gas leak detection system generates display content including indicators of remote and local potential leak source areas situated on a map of an area of a gas concentration measurement survey performed by a vehicle-borne device. The remote area may be shaped as a wedge extending upwind from an associated gas concentration measurement point. The local area graphically represents a potential local leak source area situated around the gas concentration measurement point, and having a boundary within a predetermined distance (e.g. 10 meters) of the gas concentration measurement point. The local area may be represented as a circle, ellipse, or other shape, and may include an area downwind from the measurement point. Size and/or shape parameters of the local area indicator may be determined according to survey vehicle speed and direction data, and/or wind speed and direction data characterizing the measurement point.

公开(公告)号：US10126200B1

公开(公告)日：2018-11-13

申请号：US15462533

申请日：2017-03-17

Applicant: Picarro Inc.

Inventor： David Steele ,  Sze M. Tan ,  Chris W. Rella

IPC: G01M3/04 ,  G01W1/02 ,  G01N25/20

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.

公开(公告)号：US09599529B1

公开(公告)日：2017-03-21

申请号：US14139388

申请日：2013-12-23

Applicant: Picarro Inc.

Inventor： David Steele ,  Sze M. Tan ,  Chris W. Rella

IPC: G01N31/00 ,  G01M3/04

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.

公开(公告)号：US20160011069A1

公开(公告)日：2016-01-14

申请号：US14326195

申请日：2014-07-08

Applicant: Picarro, Inc.

Inventor： David Steele ,  Eric R. Crosson ,  Sze Meng Tan

IPC: G01M3/00 ,  G01N33/00

CPC classification number: G01N33/0004 ,  G01M3/22

Abstract: This work provides event selection in the context of gas leak pinpointing using mobile gas concentration and atmospheric measurements. The main idea of the present approach is to use a moving minimum to estimate background gas concentration, as opposed to the conventional use of a moving average for this background estimation.

Abstract translation: 这项工作提供了在使用移动气体浓度和大气测量的气体泄漏精确定位的情况下的事件选择。 本方法的主要思想是使用移动最小值来估计背景气体浓度，而不是传统使用移动平均值进行该背景估计。

公开(公告)号：US11525819B1

公开(公告)日：2022-12-13

申请号：US17249795

申请日：2021-03-12

Applicant: Picarro Inc.

Inventor： Sean MacMullin ,  Chris W. Rella ,  Aaron Van Pelt ,  Alex Balkanski ,  Yonggang He ,  Sze M. Tan ,  David Steele ,  Tim Clark

IPC: G01N33/00 ,  G01M3/04

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 sources. 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.

公开(公告)号：US10386258B1

公开(公告)日：2019-08-20

申请号：US15144751

申请日：2016-05-02

Applicant: Picarro Inc.

Inventor： David Steele ,  Chris W. Rella ,  Sze M. Tan ,  Sean MacMullin ,  Anders Nottrott

IPC: G01M3/00 ,  G01M3/04

Abstract: In some embodiments, computer-implemented systems/methods detect and/or quantify changes in emission rates of gas emission sources (e.g. natural gas leaks originating from underground distribution pipelines) using data from multiple vehicle-based measurement runs. Exemplary described methods aim to address the observation that large (e.g. 10×) changes in gas concentrations away from a source may be observed even in the absence of significant changes in source emission rate, due to changes in wind or other atmospheric conditions and local spatial variations in gas concentrations. Described methods are useful for identifying large increases in the emission rate(s) of known sources, for example due to frost heave or other dislocations. Multiple runs are performed along the same survey path in closely-related conditions (e.g. same time of day, same lanes), and a statistical test (e.g. a Kolmogorov-Smirnov test) is used to identify changes in concentration reflecting changes in emission rates.

公开(公告)号：US20150007638A1

公开(公告)日：2015-01-08

申请号：US14493853

申请日：2014-09-23

Applicant: Picarro, Inc.

Inventor： Chris W. Rella ,  Eric R. Crosson ,  Michael R. Woelk ,  Sze Meng Tan ,  Yonggang He ,  David Steele

IPC: G01M3/04 ,  G01N33/00

CPC classification number: G01N33/0009 ,  G01M3/20 ,  G01M3/38 ,  G01N21/01 ,  G01N21/3504 ,  G01N21/39 ,  G01N2021/0314 ,  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. 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。 提供定性来源识别。 这些方法可以单独或以任何组合实施。