The Oil and Gas Patch: Novel Techniques for Real Time

Monitoring, Emissions Quantification, and Air Quality Impact Assessment

Eduardo (Jay) Olaguer, Ph.D.

Program Director, Air Quality Science

Houston Advanced Research Center

September 15, 2016

Explosion of Oil and Gas Development

Atmospheric Impacts of the Oil and Gas Industry

• Air Toxics (e.g., benzene, xylenes, formaldehyde, n-hexane, H2S, PAHs)

• Ozone (via NOx and VOC precursors)

• Particulates (primary soot and secondary aerosols)

• Greenhouse Gases (methane, CO2)

Statement of the Problem

• Oil and gas emissions can vary greatly in both time and space (regular vs. event emissions).

• Conventional monitoring technologies cannot overcome the undersampling problem.

• VOC speciation is difficult to measure.

• Fast air chemistry near inefficient combustion sources due to emitted highly reactive species.

• Conventional air quality models lack either resolution or near-source chemistry.

Flaring in the Bakken Shale

Flare Emissions of Reactive VOCs

Source: Knighton et al. (2012), Industrial and Engineering Chemistry Research, 51, 12674–12684.

Courtesy of Scott Herndon

Formaldehyde (HCHO) is both a HAP and a source of new atmospheric radicals that boost ozone formation.

Winter HCHO at WY Oil and Gas Fields as Measured by Satellite

~40 ppb HCHO, if boundary layer (vertical mixing) height is 100 m

110° W, 42.3 ° N

Nadir resolution: 13 km × 12 km

Courtesy of Thomas Kurosu, Harvard Smithsonian

BSEEC (2010) HCHO Measurements Quicksilver Lake Arlington

Highly Reactive Olefins

• Schade and Roest (2016) analyzed a year’s worth of hydrocarbon and other measurements in Floresville, Texas, at the Eagle Ford Shale. They identified two dominant factors in the ambient air data.

• The first factor had a relative contribution between 47% and 50% and was assigned to emissions from oil and gas activities due to the dominance of alkanes in the factor composition.

• The second factor had a relative contribution between 29% and 32% and was dominated by olefins and acetylene, with large contributions from NOx and aromatic hydrocarbons. This second factor was assigned to combustion sources, which may include urban traffic emissions as well as flaring and engine emissions from oil and gas sites.

• An analysis of factor variability with wind direction provided evidence that natural gas combustion associated with shale petroleum mining may have contributed significantly to the observed loadings of light olefins such as ethene and propene, which were sometimes correlated with alkanes.

Relevant Technologies

• HARC 3D micro-scale air quality model as the data interpretation engine for real-time measurements.

• Real-time monitoring, data broadcasting, and source attribution with a mobile laboratory.

• Remote sensing with Differential Optical Absorption Spectroscopy (DOAS).

HARC Air Quality Model

• Neighborhood scale 3D dispersion model with its own chemical mechanism (48 gas phase reactions).

• Very high temporal (~20 s) and horizontal (~200 m) resolution (with chemistry).

• Model can infer emissions from observed ambient concentrations (inverse mode) as well as to predict concentrations from emissions (forward mode).

• Uses calculus of variations and the adjoint method to perform data assimilation (4Dvar). This optimizes the information derived from both measurements and a numerical model.

Computational Fluid Dynamics

QUIC model used to simulate wind based on 3D LIDAR building morphology

Lake Arlington Compressor Station

QUIC model used to simulate wind based on permit-derived digital model

HARC air quality model used to infer HCHO emissions of 6 kg/hr from glycol reboiler based on BSEEC (2010) DNPH cartridge measurements

Real Time Monitoring of VOCs and Air Toxics Proton Transfer Reaction—

Mass Spectrometry

Real Time Data Broadcasting Real Time Data Broadcasting

Mobile PTR-MS Measurements in the Eagle Ford Shale of Texas

Flare Type Benzene

(kg) Toluene

(kg) Xylenes

(kg)

Routine (1-hr avg) 1.6 1.7 1. 1

Major (1-hr avg) 28 40 29

Permitted (1-yr avg) 100 145 191

Olaguer, et al. (2016), J. Air Waste Manage. Assoc., 66, 173–183.

Formaldehyde Source Attribution for a Texas Refinery

• Winds from QUIC model

• HARC model with chemistry (200 m, 20 s resolution) run in inverse mode based on mobile Quantum Cascade Laser measurements

• Emissions attributed primarily to fluidized cat cracking and desulfurization operations

• Formaldehyde emissions agree with I-DOAS remote sensing measurements (18 kg/hr)

Olaguer, et al. (2013), J. Geophys. Res.-Atmos., 118, 11,317–11,326.

CCD detector

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Imaging DOAS

System

Imaging Differential Optical Absorption Spectroscopy (I-DOAS)

Benzene and other Toxics Exposure (BEE-TEX) Study

• HARC conducted BEE-TEX in February 2015 together with UCLA, Aerodyne Research, Inc., U. of North Carolina at Chapel Hill, and U. of Houston.

• Long Path (LP-) DOAS remote sensing with Light Emitting Diodes (LEDs) was combined with Computer Aided Tomography (CAT).

• Exposure of cultured human lung cells to ambient air pollution, with measurement of cell protein, enzyme, and genetic responses.

• Real time source attribution using HARC model (within 1 hr of CAT scan or mobile lab measurements).

Pipeline Network, Point Sources, and Mobile Lab Measurements of Benzene in Galena Park Analysis Grid

Tanker at Kinder Morgan port terminal from 8:52 AM – 3:19 PM

Barges at Kinder Morgan port terminal from 4:23 PM – 6:41 PM

HARC Mobile PTR-MS Measurements

Galena Park Feb 19 Benzene Event Total Domain Emissions (kg/hr)

Time Period Point Sources Pipelines Total Emissions

Afternoon 16.43 34.73 51.16

Evening 5.59 10.69 16.29

2011 NEI 8.27 0 8.27

Olaguer, et al. (2016), J. Air Waste Manage. Assoc., 66, 164–172.

Computer-Aided Tomography based on LP-DOAS with LEDs

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Hartman Park

Manchester St

Figures courtesy of Jochen Stutz

LP-DOAS Measurements on Feb 19 from 4:01:27 – 4:26:20 AM LST

Path ID

Path Length (m)

Toluene (ppb)

m-Xylene (ppb)

p-Xylene (ppb)

M1 770 15.12 4.00 1.83

H4 740 2.53 0.92 0.19

H1 513 20.91 6.89 2.75

M2 1203 3.39 0.33 0.05

H2 526 39.87 11.17 4.74

M3 270 1.42 1.51 0.60

H3 689 11.05 1.95 0.64

HARC Mobile Lab Measurements on Feb 19 from 1:30 – 4:15 AM LST

Source Attribution for Manchester

~4 am, February 19, 2015

Emission Point Number (EPN)

Toluene Emissions (kg/hr)

Xylene Emissions (kg/hr)

CAT PTR-MS CAT PTR-MS

TRAIN 2.42 0.79 1.11 0.90

EPTTL 1.90 0.63 0.78 0.64

EPLLL 1.70 0.61 0.69 0.63

91AFUG 1.51 0.40 0.58 0.41

47AD5409 0.28 0.28 0.32 0.32

AREA 0.89 0.20 0.44 0.24

EPHLL 2.21 0 0.96 0.03

Toluene Plume, Feb 19 at ~4 am

EPLLL, EPHLL (Railcar loading fugitives)

EPTTL (Tank truck loading fugitives)

AREA (Equipment leaks)

47AD5409 (Wastewater stack emissions)

CAT Scan Mobile Lab-based Reconstruction

Cold Ozone Phenomenon

• Schnell et al. (2009) reported rapid formation of O3 (<30 ppb at night to >140 ppb in the PM) at −17 °C in Upper Green River Basin, WY.

• A 100 m thick boundary layer trapped ozone precursors emitted by oil and gas activities.

• Strong reflection of sunlight by snow cover accelerated ozone formation.

• Similar phenomenon in Utah was the subject of the Uinta Basin Winter Ozone Studies (UBWOS) conducted by NOAA and others in 2012, 2013 and 2014.

Horsepool

Horizontal Domain: 20 km × 20 km; Horizontal Resolution: 400 m; Vertical Domain: 0-307 m AGL; Vertical Layers: 12 (variable resolution)

HARC Model Simulation of UBWOS 2013 Ozone Episode

UBWOS Chemical Measurements

• Data from the following instruments at the Horsepool site were used courtesy of the relevant NOAA Principal Investigators: – Tower meteorological instrumentation suite

– NOx/NOy/O3 instrumentation suite

– PTR-MS for HCHO, toluene, and C2-benzenes

– Acid Chemical Ionization Mass Spectrometer (CIMS) for HONO

– GC-FID for ethene and propene.

Meteorological Assumptions

• Background horizontal wind, RH, and surface temperature based on Horsepool measurements.

• Vertical extrapolation based on a logarithmic wind profile with Z0 = 0.1 m and 1/LM-O = 0, and neutrally stable lapse rate.

• QUIC model used to horizontally extrapolate winds based on kinematic flow over topography.

• Vertical diffusivity profile assumes mixing depth of 300 m, while Kh set at 50 m2/s.

• Parameterized photolysis rates were doubled to account for the effect of snow surfaces.

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Simulated Date: January 26, 2013 Time Period: 9am – 12 pm MST Method: 4Dvar data assimilation of Horsepool chemical observations (with interpolation of missing data) Background Chemistry: Reactivity of unresolved VOCs (e.g., slowly reacting alkanes) set at 50 s-1 Boundary Conditions: Generally reflect minimum observed values during simulated time period.

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HARC model

Horsepool observations

Upwind Boundary Condition: 90 ppb

Before and After

Species Initial Domain Emissions Estimate (tpy)

Optimized Domain Emissions (tpy)

NO 1101 1112

NO2 122 215

HONO 10 (0.8% of NOx emissions) 12 (excludes snow flux)

HCHO 58 (5% of CO emissions) 94

Ethene 20 66

Propene 20 24

Toluene 20 53

Xylenes 20 57

Current and Future Work: Link to Human Health Symptoms

• HARC is working with the Alabama-Coushatta Tribe (Livingston, TX) to monitor tribal oil and gas sites and nearby residences.

• Tribal residents will broadcast human health symptoms in real time via mobile smart devices, to which mobile lab will respond.

• Seek opportunities to measure human breath samples in Tedlar bags with PTR-MS.

Conclusion

• We can now monitor oil and gas emissions and resulting air pollution plumes in real time while operating outside fence lines.

• We can now attribute observed hot spots of air pollutants to specific emission points at facilities, and quantify emissions in real time.

• We can extrapolate chemically reactive plumes beyond measurements using model-based data assimilation.