Large area airborne hyperspectral imaging for atmospheric emissions throughout the oil and gas lifecycle

Transcription

1 Large area airborne hyperspectral imaging for atmospheric emissions throughout the oil and gas lifecycle Katherine M. Saad, David Tratt, Eric Keim, Jeffrey Hall, Karl Westberg, Kerry Buckland, Tamara Volquarts, Yanina Landa, Patrick Johnson, Donald Rudy The Aerospace Corporation El Segundo, CA, USA July 12, 2018 Approved for public release. OTR The Aerospace Corporation 1 Geological Remote Sensing Group (GRSG) Oil and Gas Remote Sensing Workshop Boulder, CO

2 Hyperspectral Imagery (HSI) for Emissions Mapping HSI uniquely allows rapid scanning of large areas to detect fugitive emissions High spatial resolution (<2m) allows trace-back of emission plumes to their source Information on spatial variability Identification of a large suite of gases Quantification of emission rate is possible with knowledge of prevailing meteorology HSI can be applied to both short-term hazard scenarios and longer-term trends Situational awareness after a disaster Bridges bottom-up inventories and top-down background observation-driven estimates Alleviates NRC concerns relating to the value of airborne remote sensing for characterizing methane emissions (i.e., limited spatial coverage, see: Improving Characterization of Anthropogenic Methane Emissions in the United States, doi: /24987) El Segundo Hyperion Waste Treatment Plant 2

3 Oil & Gas Applications for HSI Measurements Upstream, Midstream, and Downstream Processes Exploration & Production Prospecting: discovery of natural oil seeps and shallow methane sources leaking from faults Existing Wells & Production Infrastructure: detection of failures/leaks in active wells, and leaks in old and abandoned oil and gas wells, tanks, and gathering systems Environmental Baseline & Due Diligence Assessment: scanning before, during, and after a specific operation differentiates methane emissions from other sources Transport Pipeline Operations: search for leaks early and often which can mitigate future damages and product loss Processing and Production Refinery Operations: ensure and document that emissions are within the range and expectations of permits and regulatory reporting standards Distribution End-Use Infrastructure: map small distributed sources dispersed throughout large (typically urban) areas 3

4 HSI Instrumentation and Capabilities Aerospace currently operates two infrared sensors Mako: Longwave infrared (LWIR) whisk-broom scanner Innovative spectrometer design (f/1.25 Dyson) results in high optical throughput High sensitivity mode: Noise reduction achieved using longer integration times Very large area coverage: 32 km 2 / min (2m GSD, 12kft AGL) MAHI: Mid-wave infrared (MWIR) pushbroom scanner Nadir-viewing, roll-stabilized mount High spectral resolution: ~3.3 nm Large area coverage: 7.6 km 2 / min (2m GSD, 7kft AGL) Mako MAHI Spectral coverage μm μm Spectral channels Spectral resolution 44 nm 3.3 nm Spatial pixels IFOV 0.55 mrad 0.94 mrad Optics temperature 9 K 78 K Noise-equivalent spectral radiance (NESR) Noise-equivalent temperature interval (NEDT) 0.3 μflick (10 μm) 0.4 μflick (4 μm) 20 mk 50 mk O 3 H 2 O H 2 O H 2 O N 2 O CO 2 H 2 O H 2 O H 2 O H 2 O CO 2 CO Wavelength (µm) VIS NIR SWIR MWIR LWIR FIR Over 700 gases are included in the LWIR and MWIR spectral libraries 4

5 Airborne Sensor Operations Whisk-broom scanning MAKO installation on aircraft DeHavilland DHC-6 Twin Otter 5

6 Ground Truth With Portable Plume Generator (PPG) Validation of mass-balance approach to plume quantification Controlled releases at 12m AGL simulate fugitive and vent emissions of gases and liquids Mass flow rates measured with Coriolis flowmeters (±2%) and recorded every second Air temperature, wind speed/direction, and relative humidity measured by met station on release mast Radiometric temperature over wavelengths of interest measured for ground surface Methane Comparison (Mako) MAHI 0.9-m GSD 6 (Tratt et al. 2014)

7 Southern California Oil & Gas Emissions Survey Data collected over by Mako and MAHI Geolocation (latitude, longitude, altitude) of image pixels with GPS Plumes detected by applying Adaptive Coherence Estimator (ACE) spectral filter to atmospherically compensated data (Buckland et al. 2017; Tratt et al. 2017) Identify source of plumes Pixels of origin determined based on spectral match values and plume geometry Source type determined from publicly available info such as the facility search in CARB Pollution Mapping Tool, Google Maps, and Vista-LA dataset (Carranza et al. 2017) and verified using thermal image and context camera Large areas can be acquired with sufficient spatial resolution to resolve individual emitters in the scene MAHI recently covered 225 km 2 in LA at sub-meter resolution over 15 hours, capturing over 100 facilities across the entire basin in high-density air traffic corridors Areal coverage rates of up to 4,300 km 2 in a single 4-hour sortie were demonstrated with Mako at 2-m GSD 7

8 Methane Emissions from Upstream Sources Leaks from multiple sources merge into single large plume, as seen by Mako Bakersfield Oil Field, May 2016 RGB thermal image CH 4 detection filter 1-m GSD 500 m Mako can quickly cover large complexes at high enough resolution to parse out individual sources 8 (Tratt et al. 2018, Proc. SPIE)

9 Methane Emissions at Well Heads Kern Oil Fields, Bakersfield, CA Mako scanned 140 km 2 in 28 min 1-m GSD 9 (Tratt et al. 2018, Proc. SPIE)

10 Methane Emissions at Well Heads (5 months later) Kern Oil Fields, Bakersfield, CA Mako scanned 140 km 2 in 28 min 1-m GSD 10 (Tratt et al. 2018, Proc. SPIE)

11 Hydrocarbon Releases from Processing Plants and Pipelines Ethene Elk Hills Oil Field, August 2013 Mako scanned 40 km 2 in 2 min 2-m GSD Methane + Ethane (Tratt et al. 2014) 11

12 Hydrocarbon Releases from Storage Tanks and Refineries Kern Storage Tank, April 2015 Est. Emission Rate of 57 MCFD ±32% West LA Petroleum Refinery, October 2017 MAHI 1.8-m GSD 12

13 Hydrocarbon Emissions from Natural Gas Fueling Stations Port of Long Beach April 2015 Thermal Image Baldwin Park (Heavy Duty Vehicles) October 2017 MAHI 1-m GSD Methane Detection Imagery/Map data 2017 Google 13 Imagery/Map data 2017 Google

14 Methane Emissions from Liquefied-Compressed NG Fueling Station Repeated and continuous emissions at the same Downtown LA Heavy Duty Vehicle site Thu 22 Jun (observed by Mako 3 times within 20 min) Wed 28 Jun (observed by Mako 10 times in ~10 min intervals; dissipated after 1.5 hours) Mako Context Camera No detectable NMHCs in the plume, indicating that they have been mostly removed from the natural gas fuel 14

15 Future Work Comparisons of sources across collects (3000 km 2 in LA with Mako) Source-type variability Temporal variability: diurnal, seasonal, episodic Improve detection and quantification algorithms to reduce uncertainties Downstream fossil processes have higher ratios than midstream processes, which have higher ratios than upstream processes Develop co-emission factors for methane Quantify NMHC plumes from sources not emitting methane Poso Creek Kern Front Kern River 15

16 Acknowledgments Campaign support: Jonathan Taylor, Jeff Matic Oil & Gas analysis: Karen Jones Data processing/analytics: Steve Young Funding The Aerospace Corporation Postdoctoral Fellowship Program Mako and MAHI aircraft campaigns: The Aerospace Corporation Technical Investment Program Contact: 16