The Oil and Gas Patch: Novel Techniques for Real Time Monitoring, Emissions Quantification, and Air Quality Impact Assessment

Transcription

1 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

2 Explosion of Oil and Gas Development

3 Atmospheric Impacts of the Oil and Gas Industry Air Toxics (e.g., benzene, xylenes, formaldehyde, n- hexane, H 2 S, PAHs) Ozone (via NO x and VOC precursors) Particulates (primary soot and secondary aerosols) Greenhouse Gases (methane, CO 2 )

4 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.

5 Flaring in the Bakken Shale

6 Flare Emissions of Reactive VOCs Source: Knighton et al. (2012), Industrial and Engineering Chemistry Research, 51, Formaldehyde (HCHO) is both a HAP and a source of new atmospheric radicals that boost ozone formation. Courtesy of Scott Herndon

7 Winter HCHO at WY Oil and Gas Fields as Measured by Satellite ~40 ppb HCHO, if boundary layer (vertical mixing) height is m Nadir resolution: 13 km 12 km 110 W, 42.3 N Courtesy of Thomas Kurosu, Harvard Smithsonian

8 BSEEC (2010) HCHO Measurements Quicksilver Lake Arlington

9 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 NO x 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.

10 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).

11 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.

12 Computational Fluid Dynamics QUIC model used to simulate wind based on 3D LIDAR building morphology

13 Lake Arlington Compressor Station HARC air quality model used to infer HCHO emissions of 6 kg/hr from glycol reboiler based on BSEEC (2010) DNPH cartridge measurements QUIC model used to simulate wind based on permit-derived digital model

14 Real Time Monitoring of VOCs and Air Toxics Proton Transfer Reaction Mass Spectrometry

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16 Real Time Data Broadcasting

17 Mobile PTR-MS Measurements in the Eagle Ford Shale of Texas Flare Type Benzene (kg) Toluene (kg) Xylenes (kg) Routine (1-hr avg) Major (1-hr avg) Permitted (1-yr avg) Olaguer, et al. (2016), J. Air Waste Manage. Assoc., 66,

18 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.

19 vertical vertical Imaging Differential Optical Absorption Spectroscopy (I-DOAS) horizontal scanning horizontal CCD detector image of number of HCHO molecules in absorption path wavelength absorption spectra for each viewing elevation Imaging DOAS System

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21 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).

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

23 HARC Mobile PTR-MS Measurements 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

24 Galena Park Feb 19 Benzene Event Total Domain Emissions (kg/hr) Time Period Point Sources Pipelines Total Emissions Afternoon Evening NEI Olaguer, et al. (2016), J. Air Waste Manage. Assoc., 66,

25 Computer-Aided Tomography based on LP-DOAS with LEDs H1 Hartman Park H2 M1 H3 M2 Manchester St H4 M3 Figures courtesy of Jochen Stutz

26 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) M H H M H M H

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

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29 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 EPTTL EPLLL AFUG AD AREA EPHLL

30 Toluene Plume, Feb 19 at ~4 am EPTTL (Tank truck loading fugitives) AREA (Equipment leaks) EPLLL, EPHLL (Railcar loading fugitives) 47AD5409 (Wastewater stack emissions) CAT Scan Mobile Lab-based Reconstruction

31 Cold Ozone Phenomenon Schnell et al. (2009) reported rapid formation of O 3 (<30 ppb at night to >140 ppb in the PM) at 17 C in Upper Green River Basin, WY. A 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.

32 HARC Model Simulation of UBWOS 2013 Ozone Episode Horsepool Horizontal Domain: 20 km 20 km; Horizontal Resolution: 400 m; Vertical Domain: m AGL; Vertical Layers: 12 (variable resolution)

33 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 NO x /NO y /O 3 instrumentation suite PTR-MS for HCHO, toluene, and C2-benzenes Acid Chemical Ionization Mass Spectrometer (CIMS) for HONO GC-FID for ethene and propene.

34 Meteorological Assumptions Background horizontal wind, RH, and surface temperature based on Horsepool measurements. Vertical extrapolation based on a logarithmic wind profile with Z 0 = 0.1 m and 1/L M-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 K h set at 50 m 2 /s. Parameterized photolysis rates were doubled to account for the effect of snow surfaces.

35 y (m) 150 Cost Function Ozone (ppb) x 10 4 HARC model Horsepool observations Time (min) O3 Final Concentration (ppb) 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. 10 x Upwind Boundary Condition: 90 ppb x (m) x Iteration Number

36 Before and After Species Initial Domain Emissions Estimate (tpy) NO NO Optimized Domain Emissions (tpy) HONO 10 (0.8% of NO x emissions) 12 (excludes snow flux) HCHO 58 (5% of CO emissions) 94 Ethene Propene Toluene Xylenes 20 57

37 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.

38 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 modelbased data assimilation.