Surface-based Methane Monitoring and Measurement Network
Pilot Demonstration: Findings from Project Astra Phase I
January 31, 2023

SUBMITTED BY
The University of Texas at Austin
Center for Energy and Environmental
Resources M/C R7100 at EME
10500 Exploration. Way
Austin, Texas 78758

PRINCIPAL INVESTIGATORDavid Allen
allen@che.utexas.edu

SUBMITTED TO
U.S. Department of Energy
National Energy Technology Laboratory

1

Contents
Summary

3

1.

Introduction

42.

Site selection and regional meteorology

6

3.

Sensor testing and intercomparisons114.

Network design17

5.

Data analytics

23

6.

Overall performance

39

7.

References

41

2

Summary
Project Astra is testing the effectiveness of a shared network of fixed methane emission monitors
in the Permian Basin oil and gas production region in West Texas. The Project Astra partnership,
formed in 2020, is led by the University of Texas Center for Energy and Environmental Resources,
and includes Chevron, the Environmental Defense Fund, ExxonMobil, GTI Energy, Microsoft,Pioneer Natural Resources, and SLB. Since 2020, the goal of the Project has been to test theeffectiveness of a shared network of fixed methane emission monitors in detecting methane
emissions from oil and gas operations. Through 2023, the project proceeded through three stages.
An initial stage evaluated the performance of methane sensing systems in nine months of fieldtesting. In a second stage, based on the performance characteristics of the sensing systems
demonstrated in field testing, network designs were developed and a network design with multiple
redundancies was created. In a third stage, the network was deployed and operated, beginning in
February 2022.
This report documents the development of Project Astra from 2020 through 2023. Sections of the
document, summarized below, describe site selection, sensor testing, network design, data
analytics, and overall performance of the network. Beginning in late 2023, Project Astra entereda second Phase, which will be documented in subsequent reports.
Site selection The Project Astra field deployment encompasses approximately 50 sites in a
~10 km2 area; the Permian Basin site was selected based on its strong, steady winds, and its
well density and productivity. The selected area, near Midland Texas, contains production
sites that are representative of the Basin.
Sensor testing Multiple commercially available sensing systems were field tested; two typesof high precision infrared sensing systems and one moderate precision metal oxide
semiconductor sensing system were chosen for network deployment
Network design The network was designed to simultaneously test (i) a dense network ofmoderate resolution sensors and (ii) a sparse network of high resolution sensors, with both
networks designed to cover the entire test region. Some individual sites were equipped withmultiple sensors to enable comparison of single and multiple site deployments. Network
design software based on dispersion modeling was developed and tested.
Data analytics Software systems were developed and tested for background correction andemission event detection. On-going work is developing tools for source attribution,
quantifying emission event durations, and reducing uncertainty in emission rate quantification.
Overall performance The sub-systems that most constrain the overall performance of the
network performance are the data analytics capabilities. On-going work is expanding thosecapabilities.

31. Introduction
Project Astra is testing the effectiveness of a shared network of fixed methane emission monitors
in the Permian Basin oil and gas production region in West Texas. The Permian basin was chosen
for this demonstration because it is the largest oil and gas production region (by volume of
production) in the United States, because the region has simple topography and strong andpersistent winds and because the proximity oil and gas production sites in the Permian Basinenhances the advantages of a shared network of sensors. Because of the proximity of sites,
emissions can be detected without having multiple sensors surrounding every site. Compared toisolated sites requiring fixed sensors located at multiple cardinal directions (e.g., sensors north,south, east and west of each site), sensors in the Permian Basin can be located at nearby sites (e.g.,sites to the north, south, east and west of a central site) to replace some of the information thatwould be provided by multiple sensors for isolated sites.
The Project Astra partnership, formed in 2020, is led by the University of Texas Center for Energy
and Environmental Resources, and includes Chevron, the Environmental Defense Fund,
ExxonMobil, GTI Energy, Microsoft, Pioneer Natural Resources, and SLB. The initial goal of the
Project was to test the effectiveness of a shared network of fixed methane emission monitors in
detecting methane emissions from oil and gas operations. Through 2023, the project proceeded
through three stages. An initial stage evaluated the performance of methane sensing systems innine months of field testing. Based on the performance characteristics of the sensing systems
demonstrated in field testing, network designs were developed and a network design with multiple
redundancies was created. The network deployment began in February 2022. Data ingestion
systems and emission detection algorithms were developed, and the network was successfully
operated throughout 2023.
In October 2023, the U.S. Department of Energy funded a next phase of Project Astra. The
objectives of Phase 2 of Project Astra are to:








Extend the Project Astra network to a demonstration of a scalable “basin-wide” platform
The types of facilities sampled will be expanded from a contiguous group of approximately
50 sites that are primarily oil and gas production pads, to include gas gathering and boostingsites and gas processing sites. The gas processing and gathering and boosting sites will be
individual sites that are not contiguous with each other or the existing network.
Advance the detection and quantification capabilities of sensing technologies Project
Astra’s initial sensing network includes multiple types of fixed position ground sensing
systems to detect, but not necessarily quantify emissions. Phase II of the project willcontinue work on emission detection, focusing on characterizing the frequency andduration of emission events, and will assess the ability of the network to quantify
emissions.
Support emission inventory improvements Expanding detection and quantification
capabilities will enable the development of emission factors, region specific emission
inventories, and characterization of the magnitudes, frequencies and durations of emission
events.
Demonstrate advanced data analytics and accelerate and automate responses to network
emission detections Project Astra currently manually performs data analytics on sensor
signals, identifying network detections on a roughly weekly basis. Detections are discussed4with operators and analyses are performed based on operator provided process data, sitelog information and on-site follow-up. These data analytics will be automated andcompared with process data and site activity data.
Inform the development of Integrated Methane Modeling Platform Designs By
documenting its processes and analysis tools, Project Astra will help inform future
monitoring system designs.
Develop education and training programs for workforce development Operation of
methane emission monitoring systems will require training. Training programs developed
through this program will contribute to workforce development.

The remainder of this document summarizes the status of Project Astra and documents its
development from 2020 through 2023. Sections of the document describe site selection, sensor
testing, network design, data analytics, and overall performance of the network.

52. Site selection and regional meteorology
2.1 Site characterization
The initial Project Astra test network is located in west Texas in the Permian Basin oil and gas
production region. Site selection criteria, for locations within the Permian Basin included:






Representative oil and gas production volumes
Representative well densities
Representative methane emissions by magnitude and source type
Access to regional air travel
Availability of sites for all three operators (Chevron, ExxonMobil, and Pioneer)participating in Project Astra in a contiguous region

Based on these criteria, a site near the city of Midland was selected for the Project Astrademonstration.
The initial demonstration region contains a total of 74 active wells on approximately 50 pads,
collectively producing 9302 Mscf/day of gas and 2254 barrels per day of oil, as of May 2023. The
average production rate per well is 125.7 Mscf/d of gas and 30.5 barrels per day of oil. These
production rates are broadly representative of production rates in the Permian Basin (156 Mscf/day
of gas and 38.3 barrels/day of oil per well). Average production rates in Midland County areslightly higher, on average, than in the entire Permian Basin, at 291 Mscf/d of gas production and83.5 barrels/day of oil production per well. The distributions of production rates for gas and oil,for the wells in the demonstration region, wells in Midland County, and wells in the Permian Basin,that were actively producing in May 2023, is shown in Figure 2-1.

6

Figure 2-1. Distribution of oil and gas production rates per well in the demonstration region,
Midland County, and the Permian Basin in May 2023.

7

The area covered by the contiguous demonstration region is 7.32 km 2. This area is defined by aconvex polygon including all the pads in the network. With 74 actively producing wells (in May2023), the well density is 10.1 wells/km2, which is higher than the Midland well density of 3.3
wells/km2 and considerably higher than the Permian Basin well density of 0.55 wells/km 2.
The well sites in the region include single well pads with limited liquids handling equipment,
single well pads with associated liquids handling equipment and flares, and tank battery sites withliquids and gas handling equipment for multiple wells located on other pads.

8

2.2 Regional meteorology and topography
The Permian basin is characterized by persistent winds with average wind speeds of approximately
8-9 miles per hour. Figure 2-2 shows the seasonal distributions in wind directions, as recorded bya Continuous Air Monitoring Station (CAMS) maintained and operated by the Texas Commission
on Environmental Quality (TCEQ, 2024). Wind directions in the spring and summer are primarily
from the southeast to southwest. In the fall and winter, wind directions are more variable; in allseasons, wind speeds average 8-9 miles per hour. Calm periods are infrequent with less than 5%of observational hours having wind speeds below 3 miles per hour in 2019 (Figure 2-3).
These distributions of wind speeds and directions indicate that sensing systems placed on thesouthern boundary of pads will generally sample upwind conditions, while sampling systems on
the northern boundaries of pads will generally detect emissions from the pads.
Figure 2-2. Wind roses documenting wind speed and direction frequency distributions, binned
by quarter, using hourly TCEQ CAMS observational data for year 2019 (TCEQ, 2024)

9Figure 2-3. Annual frequency distribution of hourly CAMS 47 wind speeds during 2019.

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3. Sensor testing and intercomparison
3.1 Findings from sensor field testing
Seven methane sensing technologies were deployed in West Texas' Permian Basin. Sensors wereoperated unsheltered from October 29, 2020 through June 22, 2021. The field test site is shownin Figure 3.1.

Figure 3-1. Field intercomparison site used in Project Astra to evaluate four methane sensing
systems.
Sensor accuracy and precision were evaluated by conducting unannounced single blind challenges
with certified gases and by comparison with continuous measurements made by an Aerodyne
Research Incorporated (ARI) QC-TILDAS instrument (Quantum Cascade-Tunable Infrared Laser
Differential Absorption Spectrometer), operated by The University of Texas (UT) and locatedwithin the trailer shown in Figure 3-1. Data accuracy was judged based on the measured sensor
concentration compared with known challenge gas concentrations and compared with coincident,
collocated air measurements by the ARI QC-TILDAS.
The metrics used in evaluating the certified gas challenges were as follows:

Rise Speed – estimated time duration in seconds for the measured concentration to reach
2/3 of the way to the maximum concentration recorded during the challenge.
 Decline Speed – estimated time duration in seconds for the measured concentration to reach
2/3 of the way to the estimated background concentration.
 Percent Target Concentration Met – the maximum concentration reached compared to thecoincident QC-TILDAS mean concentration during the challenge.
 Integration Relative to QC-TILDAS – The concentrations are integrated over time series
for the TILDAS and the sensor during the challenge.

11

Details of the intercomparison are provided in by Torres, et al. (2022). A brief summary isprovided here.
Three of the seven sensing systems tested had performance that did not merit detailed analyses.
Of the remaining four sensors (Aeris, Canary, Quanta3 and Scientific Aviation), the Aeris andQuanta3 samplers had time responses in the challenge gas tests comparable to the QC-TILDAS
instrument,. The Canary and Scientific Aviation devices responded more slowly, but still with a
1-2 minute response time. The integrated areas for the Aeris, Quanta3 and Scientific Aviation
instruments were all typically within ±20% of the integrated TILDAS response. The Canary
sensor had time integrated responses that were typically 50-70% of the TILDAS response. Data
capture completeness, defined as the fraction of time for which valid measurements were madeover the test period, was >80% for the four sensors. The Scientific Aviation sensor had 100% data
completeness over the nine months of testing.
The measurements of the sensors were also compared to the measurements of the QC-TILDAS in
the time periods between certified gas challenges. For the Aeris, Quanta3 and Canary sensors,slopes of the best linear fit of the sensor concentration to the QC-TILDAS concentration, which
had its sample inlet 1-2 meters from each of the sensors being tested, were generally >0.6, forconcentrations averaged over one minute. Linear correlation coefficients (R 2) of these twoconcentration measurements were generally >0.6. The Scientific Aviation sensor showed baseline
bias, however, when the metric used for comparison with the QC-TILDAS was the difference
between instantaneous concentration and daily minimum concentration, the bias in the Scientific
Aviation sensor largely disappeared during the nine months of field testing.
Based on the results of the sensor intercomparison and the availability of multiple sensing systems
for field deployment in the pilot, the Aeris, Quanta3 and Scientific Aviation sensing systems werechosen for use in the Project Astra pilot project. Table 3-1 and Figures 3-2 to 3-4 summarize thefeatures of these three sensing systems.

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Table 3-1. Sensing systems selected for pilot deployment
Quanta 3

Aeris

Scientific Aviation

Measurements

Configured to measuremethane only. Sensor unit also
records barometric pressure,temperature, and humidity

MIRA technology measures ethaneand methane at <1ppb/s sensitivity
level, plus H2O vapor and dry mole
fractions. C2:C1 ratio can be used todiscriminate sources.

Multi-gas, any reducing
gas; calibrated formethaneFootprint/Space

30 x 30 inches

6-9 ft2

1'x1'

Temporal resolution

1 sec.

0.5 - 1.0 sec.

0.2 sec.

Detector

Near infrared laser absorption
spectroscopy sensor based on
tunable diode laser
spectroscopy

Direct absorption mid-IR
spectroscopy in the 3 micron region.

Metal oxide sensor

Sensitivity/Range

Ambient (2 ppm) to 500 ppm(optimized for high sensitivity)

1ppb level to % concentration levels.

500 ppb over backgroundto 5000 ppm

Accuracy

Field precision is better than
20 ppb;methane and ethane: 600ppt/s
sensitivity in both Pico and Ultraversion of MIRA gas analyzers.
Accuracy: 1-2ppb drift long-term in
Ultra analyzers for methane, 1030ppb drift in Pico series. Ethane:<1ppb drift in Ultra Series and 7ppb
drift max typical in Pico series.

CH4 precision: ± 60 ppb
for a 1-minute average
CH4 accuracy: ±(1 ppm +15%)

Accuracy is rated at ~20% atbackground concentrations (2
ppmv)

13

The Aeris Technologies sensing system uses
a mid-infrared laser-based gas analyzer andhas a sample flow rate of approximately 0.30.5 lpm. The analyzer was configured for 1
Hz data sampling. Additional information isavailable at www.aerissensors.com. Figure
shows the installed Aeris sensor

Figure 3-2. Aeris sensing system

The Quanta3 sensing system measures
methane using tunable laser diodespectroscopy technology. The system
makes one measurement per second andreports data at the same frequency via
4G/LT cellular modem to a proprietary
cloud platform. The system is poweredby a solar battery recharged by solar
panels. For additional information isavailable at https://www.quanta3.com.
Figure shows the installed Quanta 3
sensing system.
Figure 3-3. Quanta 3 sensing system

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The Scientific Aviation unit is a multigas sensor using metal oxide
semiconductor technology. The system
makes 5 voltage measurements per
second and averages the 300 voltage
measurements from each minute toreport one voltage measurement per
minute via LTE cellular modem to a
cloud server. Voltage readings areconvertedtoconcentration
measurements on the server. The system
is powered by a battery recharged by an
integrated solar panel. A photo of theinstalled Scientific Aviation SOOFIE
sensor appears in Figure 3-4.Error!
Referencesourcenotfound.
Additional information is available at

Figure 3-4. Scientific Aviation sensing system(SOOFIE)

https://www.scientificaviation.com/soofie/15

3.2 Sensor intercomparisons during Project Astra pilot deployment
To continue sensor intercomparisons the three sensing systems selected for the Project Astra pilot
demonstration were deployed with multiple co-locations of sensing systems. Additional
intercomparisons of sensing systems are underway. Table 3-2 summarizes data completeness datafor 2023 (fraction of time sensing system produced data that passed initial quality assurance
screening). The percentages reported in Table 3-2 are data completeness metrics averaged overall sensing systems of a given type (Aeris, Quanta3 and Soofie).

Table 3-2. Data completeness for 2023 for the three sensing systems
Monthly data completeness (%) by sensor type for 2023,
Averaged over all sensing systems of each type in the network
Aeris
Quanta3
SoofieJanuary 2023
46.1
83.1
87.8
February 2023
19.6
83.1
89.0
March 2023
34.6
97.0
89.5
April 2023
35.7
99.3
89.7
May 2023
24.9
94.4
91.9
June 2023
9.5
98.4
95.0
July 2023
21.2
99.3
96.4
August 2023
17.0
99.7
94.8
September 2023
9.7
98.6
94.0
October 2023
6.9
97.9
96.8
November 2023
32.6
99.1
96.7
December 2023
60.4
87.9
96.7

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4. Network Design
Network design proceeded through a series of steps. An initial step determined the sensing systemdensity, expressed as sensors per site, required to be able to detect emissions throughout the
network. After determining the approximate sensing system density, sensing system locations
were selected, accounting for physical constraints on system deployment such as avoiding locating
sensing systems in the direct vicinity of site equipment. Finally, additional analyses were
performed to assess the sensitivity of emission detection to the exact placement of sensing systems
on the sites and dispersion characteristics of the emissions, such as plume buoyancy and emission
release height.
4.1 Sensor density
To evaluate the number of sensors required per site in the Project Astra pilot network, atmosphericdispersion of methane emissions was modeled for a simulated network containing 26 oil and gas
production sites. Virtual methane sensors were placed at 24 of the 26 sites, with at most one sensor
per site. Emissions were simulated from each of the 26 oil and gas production sites, over fourweek-long meteorological episodes, representative of winter, spring, summer and fall meteorology
in the Permian Basin.
In order to determine the density of sensing systems required in a network, a detection objective,
which includes a threshold emission rate and a time to detection must be specified. For the initial
sensor density calculation, the goal was to determine the sensing system density that would be
required to detect an emission rate of 10 kg/hr within one week of the onset of emissions. These
values for threshold and time to detection were selected to be consistent with the types of
specifications anticipated by the Project Astra team for regulatory applications. When regulations
were finalized by the EPA in December 2023 (US EPA, 2023), the Project Astra team began theprocess of re-examining these thresholds.
Once a detection goal is established, the limit of detection of the sensing system must be defined.
For this initial sensor density calculation, it was assumed that a sensing system could detect aconcentration enhancement of 1 ppm that persisted for one minute. Additional definitions of event
detection have been investigated and are described in Section 5. In the design calculations, thedetection threshold scales directly with emission rate. For example, a network capable of detecting
emissions of 10 kg/hr within a week of onset of emissions, using sensing systems able to detect 1
ppm concentration enhancements ,is equivalent to a network capable of detecting emissions of 100
kg/hr within a week of onset of emissions, using sensing systems able to detect 10 ppm
concentration enhancements.
The detailed analyses are described by Chen, et al. (2022). In summary, less than 1 sensor per site
was required to detect emissions from all sites in the network within a week, if emissions were
continuous at a rate of 10 kg/hr. If emissions were intermittent (1 minute of emissions per hour),less than one sensor per site was still capable of detecting emissions at all 26 sites in all four
seasonal episodes (spring, summer, fall and winter) if the detection threshold was lowered to 500
ppb. These results are specific to the site density of the region modeled in the simulations, so a
region with less dense sources was also modeled. Comparable ratios of sensors to sites were
sufficient to detect emissions from a less dense group of sources, however, more sensors are
required to detect lower emission rates. Overall, this site density analysis demonstrated that for17the conditions in the test region in the Permian Basin, less than one continuous monitor per site is
likely to be capable of consistently detecting emissions in the range of 5-10 kg/hr within a week if
a methane enhancement of 1 ppm for one minute can be reliably detected. Equivalently, one
continuous monitor per site is likely to be capable of consistently detecting emissions in the rangeof 50-100 kg/hr if a methane enhancement of 10 ppm for one minute can be reliably detected.
4.2 Initial network configuration
A requirement of one sensing system per site leads to a conceptual configuration of sensing system
placement shown in Figures 4-1 and 4-2. Figure 4-1 shows a conceptual mapping of the entire
network. Pad sites are indicated by a black circle and may be small with only a wellhead and only
a few other pieces of equipment, or may be large and contain multiple tanks, separators,
compressors and other potential sources. Based on the need for one sensor or less per site, sensing
systems were deployed to yield three configurations. Two of the networks, operating in parallel,
are designed to monitor emissions from the entire domain, encompassing approximately 50 sites
distributed relatively uniformly throughout a ~10 km 2 area. One of these networks consists of 42
moderate resolution sensors (~100-500 ppb precision) and a second network consists of 12 highresolution sensors (~10 ppb precision). These are shown conceptually as high resolution andmoderate resolution sensing systems in Figure 4-1. Both systems are designed to simultaneously
monitor the same region, allowing inter-comparisons between moderate resolution-high spatial
density networks and high resolution-low spatial density networks. Moderate resolution sensors
are co-located with all high-resolution sensors, also allowing for continued direct intercomparisons
between sensor types.
A final monitoring network, also operating in parallel, is designed to examine emissions at
individual sites (rather than the network of ~50 sites), with at least three sensing systems locations
at each of the individual sites being monitored. This is shown conceptually in Figure 4-2.
With three parallel networks deployed in the same region, there is redundancy in sampling
locations and capabilities in the current network, and the network allows for inter-comparison ofmultiple monitoring strategies and multiple sensing system types. The number of sensors deployedin each network was greater than minimum number required based on the modeling analyses, sothat multiple strategies for deploying sensors could be evaluated. Sensor locations wereconstrained to be on operator owned pads, since lease agreements often would not allow placementof sensors off the pad. The sensor networks have been in operation since March, 2022.

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Figure 4-1. Conceptual mapping of
the Astra network. Pad sites areindicated by a black circle and may
contain multiple sources. Moderate
resolution sensors are indicated inorange and high resolution sensors
are indicated in blue.

1 km

Figure 4-2. Conceptual
mapping of a single sitewith multiple sensors.
Sources are indicated byblack dots and sensors
are indicated by orange
dots. Note that sensor
locations are frequently
constrained by equipment
positioning on pads.

100 m

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4.3 Sensitivity of network design to time to detection and source characteristics
The sensing systems deployed in Project Astra operate continuously, however, a discrete numberof sensors, such as those shown in Figures 4-1 and 4-2, will not be able to detect emissions fromall sources at all times. Emission plumes may pass between sensors or above sensors. A network’s
efficiency in detecting emissions will depend on meteorological conditions, sensor detection
limits, the number of sensors deployed, and sensor placement strategies. The Project Astra teamdeveloped an approach to assess the effectiveness of sensor networks in detecting emission events.
The method is described in detail by Chen, et al. (2023a,b), and is demonstrated here using a casestudy of a group of 9 different sources at varying heights and locations on a single pad. Using site
specific meteorological data and dispersion modeling, the time periods when a sensing system isunable to detect emission events was determined.
Figure 4-3 shows the prototypical site configuration used in the analysis. This configuration istypical of a tank battery, which includes compression and liquid handling and processing
equipment for multiple production wells. The sources include tanks, compressors, a flare,
dehydrators, pumps, separators (including associated pneumatic devices), and other equipment. A
detailed description of the sources on this prototypical site are provided by Chen, et al. (2023a).
Of the 9 sources, some are non-buoyant and release emissions near ground level. Other sources(tanks) have non-buoyant emissions that are released at ~5 m above ground level (AGL). Stillother sources (compressor exhaust and flares) release emissions at higher elevations and thereleases are buoyant. Also shown in Figure 4-3 are four potential sensor locations. The placements
were restricted to the periphery of the site. This is often a practical constraint encountered in thefield, so that the sensing systems do not interfere with operator access to equipment. The sensor
locations selected were the placements that led to the highest averaged probability of detection
across the 9 sources at the site. These sensor locations might not be ideal for individual emission
source locations at different positions around the pad; however, this is a trade-off that is typical offield deployments.

Figure 4-3. A typical tank battery site configuration with multiple emission source locations andfour sensor locations arrayed around the perimeter of the pad.
Dispersion modeling was performed using the same methodologies reported by Chen, et al.
(2023a). Briefly, CALPUFF v7.2.17 was used to estimate concentrations at sensor sites with a 1
min time resolution. The primary meteorological data set was 1-minute wind speed and direction
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data collected by six meteorological stations in the Project Astra network. Meteorological datafrom March 26th to April 8th 2022 (local standard time, LST) was selected as the primary data set
for this work, as the wind directions across this 2-week period are broadly representative of annual
weather patterns (Chen, et al., 2023a). Dispersion of sources emitting methane at a rate of 100
kg/hr were modeled.
If the dispersion modeling results indicated that a sensor would be exposed to a backgroundcorrected methane enhancement of 10 ppm or more for at least 1 min, the emission was assumed
to be detected. The detection threshold of 10 ppm was based on the performance of multiple typesof sensors deployed in field testing in the Permian Basin. The primary event detection thresholdof a methane enhancement of 10 ppm over a one minute period could also be supplemented witha persistence criterion. Chen, et al. (2023b) present an analysis that combines a detection criterionof a 10 ppm methane enhancement for one minute with an average 5 ppm enhancement over a 15
minute period.
Table 4-1 shows the average time to detection for each source in Figure 4-3, if one (S1 in Figure4-3), two (S1, S2 in Figure 4-3), three (S1, S2, S3 in Figure 4-3) or four sensors are deployed.
Table 1 reports average times until detection for 100 kg/hr release events starting at a random time.
Sources with emissions that are buoyant (e.g., compressor exhaust and flares) have longer times
to detection than non-buoyant emissions. The average time to detection averaged over all sources
is 12 hours. The full range of times until detection range from 0 minutes, if the sensor is able todetect the emission at the moment it starts, to a maximum of ~83 hours (3.5 days), for thecompressor if only one sensor is on the site. More details are provided by Chen, et al., 2023b.

21

Table 4-1. Average time to detection for a large emission event at a tank battery site
Time (hours) until detection
Sourcefor different numbers of sensors on the model site
1 Sensor
2 Sensors
3 Sensors
4 Sensors
25
16
13
13
Compressor
7.9
4.3
3.0
2.7
Dehydrator
19
16
11
7.4
Flare
5.6
3.2
2.4
1.6
Fuel gas scrubber
12
7.3
5.8
5.8
Inlet scrubber
9.6
3.1
2.9
2.2
LACT
9.7
3.2
3.1
2.0
Separator
9.2
3.6
2.8
1.9
Tank
7.2
4.0
2.7
2.2
Pump
Average over all sources
Minimum
Maximum

12
5.6
25

6.8
3.1
16

5.2
2.4
13

4.3
1.6
13

The average values of time to detection of a large release event for this prototypical complex site
provide guidance on how effective continuous monitoring systems could be in constraining thetime to detection of large release events. The overall duration of an event is the sum of the time
to detection and the time to mitigation. Since the time to mitigation will be determined by the
relatively well-defined time between detection and the mitigation action, time to detection
represents the largest uncertainty in estimating the duration of a large event. The analysis showsthat even deployment of just one sensor per site would lead to an average time to detection, frommultiple sources, of roughly a day or less.

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5. Data analytics
The data streams from the sensors deployed in the test region have been integrated into a data
network. In the case of the Scientific Aviation metal oxide semiconductor sensor, a backgroundcorrection was applied to the signal; the Quanta3 and Aeris sensing systems did not require
background corrections. Once processed and background corrected, the sensing system signals
were searched for event detections. Meteorological data and dispersion modeling were used to
identify potential sources, and to perform preliminary characterization of the duration of select
events.
5.1 Sensor background corrections and event detection
Data from the field intercomparison was used to determine whether the sensing systems would
require background correction. The background in the field intercomparison was determined using
continuous (1 Hz) measurements made by an Aerodyne Research Incorporated (ARI) QC-TILDAS
instrument (Quantum Cascade-Tunable Infrared Laser Differential Absorption Spectrometer),operated by The University of Texas (UT) and located within the trailer shown in Figure 3-1. The
Aerodyne QC-TILDAS instrument was deployed in a multi-port sampling mode. The multiple
ports sampled ambient methane concentrations at nine locations on the sampling site, withinapproximately two meters of the inlet to each sensor. Each sample port had a dedicated samplingline and ambient air was continuously drawn through the lines by a vacuum pump. All of theindividual sampling lines were delivered, with continuous sample flow, to a multi-port sampling
valve and then, sequentially, to the QC-TILDAS instrument. The timing of the sequencing was
designed to rapidly cycle through the sample locations. Every 12 minutes, the QC-TILDAS
switched its sample line channel from one sensor to another. A 12-minute period was selected tobalance the small amount of data loss as sample lines are switched with the different measurement
durations among the sensors, with the practical need to simplify later matching up QC-TILDAS
measurements with the sensor measurements. Details are described by Torres, et al. (2022).
Figure 5-1 shows representative data, comparing the signals from the QC-TILDAS to the signals
from the Aeris, Quanta3 and Scientific Aviation sensors. Data from a representative week ofsampling is shown. Because of the sequential sampling, the comparisons between individual
sensors and the QC-TILDAS are for different time periods during the week-long period,
The data shown in Figure 5-1, and other data collected throughout the sampling (Torres, et al.,2022) indicated that the Aeris and Quanta3 systems (infrared detection) would not require abackground correction. The Scientific Aviation system, however, with its metal oxide
semiconductor sensor, showed a baseline bias that was related to atmospheric water content.

23

Figure 5-1. Comparisons of atmospheric methane concentrations measured by the QC-TILDAS
system with the Scientific Aviation (upper), Quanta3 (middle) and Aeris sensors (Torres, et al.,
2022)
24

While Figure 5-1 suggests a relatively stable background offset for the Scientific Aviation sensing
system, field deployment indicated the possibility of rapid background changes. Figure 5-2 shows
a comparison between co-located Scientific Aviation and Quanta3 sensing systems, with rapid
background fluctuations for the Scientific Aviation sensing system that are not observed for the
Quanta 3 sensing system.
Figure. 5-2-Month-long methane concentration time series for a metal oxide sensor withbackground fluctuations (upper), compared to a co-located infrared sensor with a more stablebackground (lower)
Metal oxide
sensorEmission event

Co-locatedinfrared sensor

25

Backgroundfluctuation

Multiple baseline correction algorithms were evaluated for the Scientific Aviation sensing system.
Among the approaches that have been evaluated are:
Method 1: This background correction, recommended by the sensing system manufacturer,
operates under the presumption that the location of an emission plume will vary significantly
enough over a 15 minute period, so that the minimum concentration over a 15 minute period will
be an effective background concentration for that 15 minute period. To calculate backgroundadjusted methane concentration, the background concentration is subtracted from the raw
concentration value of sensor. Then the assumed global background methane concentration (2ppm) is added to the result. As shown in Figure 5-3, this approach can result in numerous false
positive event detections if wind direction and relative humidity change significantly over a periodof less than 15 minutes.

Figure 5-3. Absolute methane
concentrations
fromtheScientific Aviation Soofie sensor(upper)andbackgroundcorrected (Method 1) methane
concentration (lower). Method 1
corrections, recommended by thevendor, is the minimum
concentration over each 15
minutes period; this time period
had relatively few events
detected by collocated infrared
sensors26

Mehod 2: In this background correction, the predicting sensor background concentrations isestimated based on atmospheric temperature and humidity using a machine learning model
developed at the University of Texas. The success of this approach depended on the learning data
set. The approach was reasonably successful (see Figure 5-4) if the learning data set spanned thesame time domain as the test data set, but if a learning data set was used to predict future data, theapproach was far less successful, suggesting that background concentrations were being influenced
by parameters other than temperature and relative humidity.

Figure 5-4. Background methane concentrations predicted by a machine learning model(Method 2).
Method 3: In this approach the background corrections estimated using Method 1 were comparedacross multiple sensors and were found to be highly correlated. Therefore, an initial background
was calculated using Method 1 and was also calculated based on an estimate from a nearby sensor’s
absolute concentration, based on the correlation between sensors. An example of this backgroundcorrection is shown in Figures 5-5, 5-6 and 5-7. While this resolved some of the issues with
Method 1, it was observed that the correlations between sensors could evolve over time. Thisapproach might also produce false negatives for very large emission plumes that are
simultaneously observed by multiple sensors.

27

Figure 5-5. Representative correlation
betweenbackgroundmethaneconcentration, predicted using Method 1,
for two Soofie sensors located on nearby
sites. Background concentrations formany sensors had high degrees ofcorrelations, but the correlations could
vary over time and varied depending on
when the sensor was acquired.

Figure 5-6. Methane concentrations without background correction for a sensor detecting an event(left) and concentrations for a nearby sensor with a highly correlated background concentration(right)Figure 5-7. Background corrected
sensor signal for the sensor in the left
hand panel of Figure 5-6, based on the
background detected by a nearby sensor(right hand panel in Figure 5-6)

28

Method 4: This approach combines emission event detection with background correction andcompares rates of change in relative humidity to rates of change of methane concentration. Thisapproach is still being evaluated, but shows promise.

Figure 5-8. Events (blue lines) identified as periods when changes in methane concentration are
not accompanied by a change in atmospheric water content.

Once signals are background corrected, a variety of algorithms for event detection can be applied
to sensor data. Because of the on-going evaluation of background corrections, initial evaluationsof event detection algorithms have utilized synthetic data, generated using atmospheric dispersion
modeling. A typical scenario is illustrated in Figure 5-9. A centralized source on a pad has
continuous emissions, generating a time series of methane enhancements at a sensor location,
estimated using dispersion modeling.

Figure 5-9. A liquid tank, located in the center of a pad (left) is assumed to emit at a rate of 100
kg/h, resulting in a signal at a sensor located on the pad (right); concentration enhancements dueto the emission source, observed by the sensor, are estimated using dispersion model. The signal
is used to evaluate event detection algorithms.
29

Three types of event detection have been evaluated:
1. Events defined based on the concentration enhancement exceeding a threshold
2. Rate of change of methane concentration exceeding a threshold
3. A time averaged concentration enhancement exceeding a threshold
Figure 5-10 shows results from methods 1 and 3, using the synthetic data illustrated in Figure 5-9.
This work is beginning and will be expanded to include other algorithms and synthetic emissions.

Figure 5-10. Two different methods for event detection applied to the same synthetic time series;
the left panel shows a 10 ppm and 100 ppm concentration enhancement threshold applied to thesynthetic time series of Figure 5-9. The right panel shows a time averaged threshold (an averageof a 5 ppm enhancement over a 15 minute time period) for the same time series; the event
detections are similar for the two methods.

30

5.2 Establishing event detection limits
The U.S. Environmental Protection Agency has established a minimum detection limit (MDL) for
continuous monitoring systems (CMS) for methane emissions in its recent OOOOb/c regulations(U.S. EPA, 2023). That MDL is specified as 0.4 kg/s, however, because continuous monitoring
systems do not directly measure emission rate, the detection limit of a CMS device is moreaccurately specified as a concentration enhancement detectable by a sensing system. Converting
between MDLs based on emission rate remote from a sensor and concentration enhancement
requires assumptions regarding meteorological conditions, the characteristics of emissions at thesite, the positioning of CMS devices in relation to the emission sources, and the amount of timeallowed for the CMS device to detect an emission source. A MDL based on emission rate isapplication and time period specific. Dispersion modeling was used to develop robust detection
limit definitions for CMS (Chen, et al., 2024).
Dispersion modeling was performed based on deployment of sensors in a radial pattern, as shownin Figure 5-11. CMS devices locations were in concentric rings, with a sensor deployed every 45
degrees, at 10m, 20m, 30m, 40m and 50m from a source. The source was assumed to emit at the
EPA defined emission threshold of 0.4 kg/hr, with a release height of 2.4 m agl. Sensors wereassumed to be located at 2.4 m agl. Simulations were also performed at emission release heightsof 5.5 m, which is representative of a release from the top of a storage tank.

Figure 5-11. Source and sensor
locations are deployed radially, inconcentric rings every 45 degrees,at 10m, 20m, 30m, 40m and 50m
from a source emiting at 0.4 kg/hr.

Dispersion modeling was performed using the same methods used to establish time to detection,
described in Section 4. The procedures are described in detail by Chen, et al. (2024). The
dispersion modeling simulations were used to generate mappings of the temporal coverage forvarious sensor configurations. Figure 5-12 shows the fraction of time that a 0.4 kg/hr emission

31source with a release point located 2.4 m agl would be observed by at least one of 8 sensors
deployed every 45 degrees, at 20 m from the source, if the detection threshold was set at 1000 ppb.

Figure 5-12. Time series of periods of detection and non-detection for a group of 8 sensors
located 20 m from a source with a release rate of 0.4 kg/s and a release height of 2.4 m agl.
Similar time series were developed for multiple distances and detection thresholds. As shown in
Figure 5-13, the temporal coverage depends on the concentration enhancement detectable by thesensing system and the distance from the source. At a 10 meter distance from the source, a sensing
system able to detect a methane concentration enhancement of 2 ppm would detect emissions only
about a quarter of the time, while a system capable of detecting a 200 ppb enhancement would
detect emissions nearly all of the time.

Figure 5-13. Fraction of time that a 0.4 kg/hr emission source with a release point located 2.4 m
agl would be observed by at least one of 8 sensors deployed every 45 degrees at various distancesfrom the source32

If effectiveness is defined as a temporal coverage >30%, then only sensing systems with a detection
threshold of 1 ppm or lower, at a 10-meter distance from the source, would be effective. If,
however, effectiveness is defined using a time to detection metric, and effectiveness is defined as
an average time to detection of 12 hours or less, then, as shown in Figure 5-14, systems with awide range of detection thresholds up to 5ppm could be effective, at a 20-meter distance from thesource. As shown in Figure 5, with lower detection limits (concentration enhancement threshold<2ppm), time to detection is more sensitive to detection thresholds rather than distance from thesource.

Figure 5-14. Detection times for a 0.4 kg/hr emission source with a release point located 2.4 m
agl by a group of 8 sensors deployed every 45 degrees at various distances from the source
The dispersion modeling simulations performed using a source height of 2.4 m agl were repeated
for a source height of 5.5 m agl. Results are described by Chen, et al. (2024). In general, temporal
coverage was lower and time to detection higher, compared to the results for a source release heightof 2.4 m. In addition, the relative performance of sensors between 10 and 20 m from the source
was complex. For emission sources at the same height as the sensors (2.4 m agl), the temporal
coverage and time to detection show relatively simple monotonic behavior, as illustrated in Figures
5-13 and 5-14. In contrast, for sources at 5.5 m agl, plumes may or may reach the sensor height at
distances 10 m downwind, leading to complex detection characteristics depending on the methane
concentration enhancement assumed for an emission detection and the meteorological conditions.
This sensitivity to emission release height characteristics means that CMS performance and MDL
will be sensitive to the mix of source characteristics.
The dispersion modeling demonstrates that the detection limit for continuous monitoring systems,
expressed as an emission rate, will depend on meteorological conditions, the characteristics of33

emissions at the site, the positioning of CMS devices in relation to the emission sources, and theamount of time allowed for the CMS device to detect an emission source. Work is on-going indeveloping systematic procedures for defining reproducible detection limit testing protocols.

345.3 Source attribution
Once an event is detected, the event is attributed to a source based on wind direction. There are
multiple challenges associated with using wind directions to attribute emission events to sources.
First, as shown in Figure 5-15, there may be multiple potential sources along a single backtrajectory indicating the direction of flow from source to sensor. Figure 5-15 illustrates a scenario
where winds are from the west southwest, with a back trajectory pointing from the east northeast.
In this scenario, an event detected by sensor S4 could be from either of two sources along the backtrajectory. Uncertainty in the distance from source to receptor will lead to uncertainties in emission
quantification. It is possible to use additional characteristics of sensor signals at the site todistinguish between different sources along the same back trajectory. For example, the twosources that are on the same back trajectory when observed by sensor S4, would not be on thesame back trajectory when observed by sensors S1 and S2. If the emission event persists until it
can be detected by multiple sensors, a triangulation of the source position can be performed
reducing uncertainty in source attribution. Nevertheless, using a wind direction from a single
sensor at a single point in time will introduce uncertainty in source attribution.
Wind direction from the west south west
Back trajectory for winds out of the west south west

Figure 5-15. Multiple sources might occur along a single back trajectory, as shown for the backtrajectory from sensor S4.

35

A second source of uncertainty in source attribution is variability in wind direction. Since winddirection can vary, even over the relatively short amount of time required for emissions plume to
be transported from the source of emissions to the sensor, there is variability in the direction oftransport from source to sensor. A single wind direction at a single moment in time may notindicate the true direction of transport. If an average distance from emission source to sensor is40-200 meters and the average wind speed in the Permian Basin is 4 m/s (9 mph) then the averagetime for transport from source to receptor is 10-50 seconds. This uncertainty can be characterizedby calculating the standard deviation of the wind direction, measured at 1 second intervals over a
one-minute period. Data for the standard deviation of wind direction in the Permian Basin,measured at one second intervals over a one-minute period, as a function of wind speed, is shownin Figure 5-16. Variability in wind direction increases as wind speed decreases. Neglecting windspeeds below 2 m/s, most sub-minute scale variability in wind directions have a standard deviationof 10-15 degrees. This means that to capture 95% of typical variability in wind direction over a
one minute period, a back trajectory cone would have a width of approximately 45 degrees.. Thisis shown conceptually in Figure 5-17.
Figure 5-16. Standard deviationofone
secondresolution
measurements of wind directions
over a one minute averaging time,
as a function of wind directions;
measurements made at sensor
locations in the Project Astra
network

Figure 5-17. Band of possible
emission source directions (redlines) associated with aneasterly back trajectory (redarrow); the approximate 95%
confidence limits of possible
source directions span an angleof 45o due to short term (1second) fluctuations in winddirections)36A third source of uncertainty in source attribution is due to plume width. As an emission plume istransported from source to sensor, the plume broadens, and the sensor may not detect the centerlineof the plume. This means that taking the wind direction at the time of a detection event may
generate a back trajectory that points toward the wrong source. This is shown conceptually in
Figure 5-18. This source of uncertainty in source attribution will be greatest at low wind speeds
when wind direction is also most variable. At higher wind speeds, sub-minute level variability inwind direction will allow sensors to sample multiple points in a plume, and by using winddirections at time of maximum observed concentration to calculate back trajectories, thisuncertainty can be minimized.

Figure 5-18. The outer edge of a plume released from the tank at the center of the site, with awind from the west, is detected by sensor S4; if the back trajectory (red dashed line) is based on
the wind direction, the source would be misidentified.

37

5.4 Emission rate quantification
Moving from a focus on emission event detection to emission quantification is an ongoing activity
and will require improving the accuracy of (i) source attribution; (ii) event duration, and (iii)
dispersion modeling.
Uncertainties associated with source attribution were described in Section 5.3. If the location of
the source is not known accurately, then the distance to the source and the portion of the plumethat a sensor is detecting will not be known. These uncertainties will both introduce uncertainties
into the calculations used to convert a sensor signal into an estimated emission rate.
Uncertainties in event duration will be directly related to quantification of the mass released in anemission event. If there are large gaps in the signal from a source (see Figure 5-12), there will be
uncertainties in estimating when an emission event began and when it ended, even if the emission
rate can be precisely measured when the sensors are able to detect the source.
Once an event is detected, dispersion models are used to convert the concentration enhancement
detected by the sensor into an emission rate. This requires accurate dispersion modeling. To
perform a preliminary assessment of the accuracy of relationship between emission rates andsensor observed concentrations, four different formulations of dispersion models, each using thesame input data, were used to simulate the emission scenario illustrated in Figure 5-9. The results
are illustrated in Figure 5-19. This simple example illustrates that a single emission rate might beassociated with concentrations at a sensor that could vary by a factor of 2-5 or more, depending on
conditions.

Figure 5-19. Predictions of observed sensor concentrations for the case study illustrated in Figure
5-9, using four different dispersion model formulations [PG = Pasquill-Gifford method of
calculating dispersion coefficients]; the concentration to emission rate ratio can vary by a factor of
2-5 or more, depending on conditions. Upward arrows indicate predictions beyond the verticalscale.
38

6. Overall performance
The overall Project Astra technology readiness assessment (TRA) system is illustrated in
Figure 6-1, where critical components are highlighted in yellow.

Figure 6-1. Project Astra technology readiness assessment (TRA) system with criticalcomponents highlighted in yellow
The overall status of the system components is summarized in Table 6-1. As described in Table
6-1, commercial methane sensing systems have different characteristics and different cost andperformance levels, but multiple systems are commercially available. Sensor deployment
strategies are also at a relatively mature level, as described in Section 4. Communications systems,
as part of the sensing systems are already integrated into commercial products, althoughcommunications coverage may still be challenging in some remote areas. Meteorological datacollection systems are commercially available, however, while the data are broadly available, thedata analytics systems needed to use these data in emission detection and quantification stillrequires improvement as described in Section 5. Improving data analytics is the main focus of theongoing work of Project Astra.

39

Table 6-1. Technology readiness summary (TRA) for Project Astra subsystems

40

References
Chen, Q., Modi, M., McGaughey, G.M., Kimura, Y. McDonald-Buller, E.C., Allen, D.T.,
Simulated methane emission detection capabilities of continuous monitoring networks in oil andgas production regions, Atmosphere, 13(4), 510, doi: 10.3390/atmos13040510 (2022).
Chen, Q., Schissel, C., Kimura, Y., McGaughey, G., McDonald-Buller, E.C. Allen, D.T.,
Assessing detection efficiencies for continuous methane emission monitoring systems at oil andgas production sites, Environmental Science & Technology, 57, 1788–1796 doi:
10.1021/acs.est.2c06990 (2023a).
Chen, Q., Kinura, Y., and Allen, D.T., Determining times to detection for large methane release
events using continuously operating methane sensing systems at simulated oil and gas production
sites, submitted to Environmental Science & Technology Letters, preprint at ChemRxiv, doi:
10.26434/chemrxiv-2023-p8lfk (2023b).
Chen, Q., Kinura, Y., and Allen, D.T., Defining detection limits for Continuous Monitoring
Systems for methane emissions at oil and gas facilities, in preparation (2024).
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operations, Preprint amt-2022-24 (2022).
United States Environmental Protection Agency, (US EPA 2023b) Standards of Performance for
New, Reconstructed, and Modified Sources and Emissions Guidelines for Existing Sources: Oiland Natural Gas Sector Climate Review, available at:https://www.epa.gov/system/files/documents/2023-12/eo12866_oil-and-gas-nsps-eg-climatereview-2060-av16-final-rule-20231130.pdf accessed December 26, 2023.

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