Picarro, Inc.

David Steele

Sze Meng Tan

Chris W Rella

Yonggang He

Aaron Van Pelt

Sean Patrick MacMullin

Eric R Crosson

Tim Clark

Alex Balkanski

Michael R Woelk

Anders Nottrott

Chris W Relia

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.

Leak detection event aggregation and ranking systems and methods

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.

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.

Gas detection systems and methods using measurement position uncertainty representations

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.

Gas leak detection and event selection based on spatial concentration variability and other event properties

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.

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

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&#xd7;) 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.

Systems and methods for detecting changes in emission rates of gas leaks in ensembles

Systems and methods for likelihood-based mapping of areas surveyed for gas leaks using mobile survey equipment

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.

Methods for gas leak detection and localization in populated areas using two or more tracer measurements

In some embodiments, vehicle-based natural gas leak detection methods include assembling a collection of measured concentration peaks originating from a common natural gas leak according to wind direction, wind variability and inter-peak distance data, and selecting from the collection a subset of one or more representative peaks for display. Assigning peaks to a collection may be performed according to a peak overlap condition dependent upon a scaling (overlap) factor which scales the spatial reach of a peak, and according to a wind condition which determines whether a downwind event points toward an upwind event. The scaling factor may depend on wind variability and on an orientation of an inter-peak vector relative to a representative wind direction. Peak filtering is particularly useful in urban environments, where buildings channel gas plumes and one leak may lead to sequential detections of multiple concentration peaks along a path.

Systems and methods for assembling a collection of peaks characterizing a gas leak source and selecting representative peaks for display

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.

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San Francisco, CA, United States of America

David Steele