AWMA 2016 Critical Review: Emissions from oil and gas operations in - - PowerPoint PPT Presentation

David Allen Department of Chemical Engineering, and Center for Energy and Environmental Resources University of Texas at Austin

AWMA 2016 Critical Review: Emissions from oil and gas operations in the United States and their air quality implications

What have we learned from 15 years

• f VOC, NOx and GHG emission

studies along the oil and gas supply chains?

Multiple approaches for measurement

(bottom‐up and top‐down)

• Direct measurements of

sources

• Fixed ground

measurement network

• Mobile ground

monitoring

• Aircraft monitoring
• Satellite measurements
• Different approaches

provide complementary information

What do the measurements tell us?

• Spatial variability in

emissions

• Temporal variability in

emissions

• Super‐emitting sub‐

populations

Spatial variability in emission magnitudes by equipment or operation type

Spatial variability in emissions per pneumatic controller Spatial variability in emissions due to liquid unloadings

Spatial variability in emission compositions within production basins

Barnett Shale (Texas) ethane and propane to methane ratios Oklahoma % VOCs in produced gas

Spatial variability in the nature of emissions between regions and on scales as small as a few kilometers

Temporal variability of emissions on multiple time scales, from minutes to years

• Changes in equipment on site and in reservoir

characteristics as wells age

• Shorter term emission variability associated with

planned episodic emissions

– Start‐ups – Shutdowns – Blowdowns, separator dumps – Pneumatic controllers – Liquid unloadings

• Unplanned emission events

What do the measurements tell us?

• Recent reviews by Miller, et

al (2013) and Brandt, et al. (2014) conclude that top‐ down data indicate methane emissions are higher than current bottom‐up estimates (a 50% increase over current bottom‐up anthropogenic emissions from all sources)

• Geographical variability

Synthesis

• Emissions have significant spatial and

temporal variability

• Magnitudes of activity and emissions are

changing

• Some bottom‐up and top down approaches

lead to different assessments of emission magnitudes

Super‐emitters

A general finding emerging from many studies of the emissions from

• il and gas supply chains

What is a super‐emitter?

What is a super‐emitter?

• 10% of the passenger

vehicle fleet on the road generates approximately 50% of total passenger vehicle emissions

• Super‐emitters caused by

age, modifications, and equipment malfunction

Super‐emitters in the oil and natural gas supply chains: Methane emissions

• New findings that sub‐

populations of sources methane emissions along the natural gas supply chain (super‐emitters) dominate emissions in many source categories

Super‐emitters in the oil and natural gas supply chains

• 2% of sites in the

Barnett shale lead to >50% of the emissions

• 19% of pneumatic

controllers lead to 95%

• f emissions
• 3‐5% of wells with

liquid unloadings account for more than half of emissions……..

1% of annual natural gas supply chain emissions from one large leak at a storage facility near Los Angeles (equivalent to >10,000 wells in routine operation)

Case study of a super‐emitting sub‐population:

Liquid unloadings (wells without plunger lifts)

0% 10% 20% 30% 40% 50% 60% 70% <1 1 to 10 10 to 100 100 to 1,000 1,000 to 5,000 5,000 to 10,000 > 10,000 Percent of Sample Annual Methane Emissions Range (MSCF/yr)

Wells without Plunger Lift (32 total)

Sampled Emissions Sampled Wells

Numbers of devices in various emission bins are well distributed about a mean value, but distribution of emissions is dominated by high emitting sub‐population

Super‐emitters in the oil and natural gas supply chains

• Super‐emitters and

emission events known to

• ccur (batch or episodic

processing, upsets, periodic maintenance, blowdowns, process start‐ups, process shut‐downs) but have been poorly quantified

• First detailed analyses of

super‐emitters in the oil and gas supply chain done for downstream processing in Houston

Texas Air Quality Study (TEXAQS ‐ 2000)

Provide scientific basis for air quality management strategies in southeast Texas

(www.utexas.edu/research/ceer/texaqs/) (www.utexas.edu/research/ceer/texaqsarchive)

Emission events (episodic super‐emitters) in Houston cause transient ozone events

Houston Dallas‐Fort Worth

Evolution of Houston ozone spike

Wind Industrial source region Ozone plume

TexAQS addresses key areas of uncertainty

• Chemical and physical processes in the

atmosphere, particularly those leading to rapid and efficient ozone formation (a.k.a.

• zone “spikes”)

TexAQS data played a crucial role in understanding these events

TexAQS addresses key areas of uncertainty

• Chemical and physical processes dominated

by highly reactive volatile organic compounds (light olefins) that were being emitted in much greater quantities than expected

• This led to a search for missing emissions

The missing emissions

• Findings from TexAQS

began the search for missing emissions

• They were found in

barges, tanks, loading and off‐loading, flares, emission events,…….

• New technologies were

demonstrated that made finding the emissions possible

Super‐emitters: Continuous or episodic emissions?

Self reporting of process emissions

• Based on findings on emission variability, new

emission event reporting required beginning in 2003

Harris, Galveston, Chambers, and Brazoria Counties VOC Event Emissions as Reported Jan 31 - Dec 31, 2003 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000 730 1460 2190 2920 3650 4380 5110 5840 6570 7300 8030 8760 Yearly Hour Emissions (lbs/hr) Event Emissions 2001 Annual Avg 10,359 lbs/hr Jan 1 Dec 31 64,860 lbs/hr 53,983 lbs/hr 86,557 lbs/hr 64,539 lbs/hr Total Event Emissions = 4,035,322 lbs

• Total mass of over 4 million pounds (2000 tons) contributes 4% to

the 45,000 tons of VOC emitted over a single year from point sources in the four counties.

• 14 times (18 hours) during the eleven‐month period, event

emissions exceed the annual average for all facilities in the region. VOCs

4,000,000 lb

What are the characteristics of the events in terms

• f time, space, and composition?

All HRVOC Events as Reported Jan 31 - Dec 31, 2003 100 200 300 400 500 600 0-24 24-48 48-72 72-96 96-120 120-144 144-168 >168 Event Duration (Hours) Number of Reported Events Unscheduled Scheduled

Most events last less than a day, many last less than an hour Largest number of events is from events of 100-1000 lb, but most of the mass is associated with events greater than 1000 lb, which occur,

• n average, several times per week

As Reported Jan 31 - Dec 31, 2003 160 375 142 33 1 100 200 300 400 500 0-100 100-1000 1,000-10,000 10,000-100,000 >100,000 Mass of HRVOC per Event (lbs) Number of Events Frequency of HRVOC events by HRVOC mass

2-3 times per week Daily Less than 24 hours

Conceptual issue

In most of US, industrial emissions are relatively constant or are small enough that meteorology is cause of “worst conditions” In Houston, both meteorology and emissions are cause of “worst conditions”

An album of emission snapshots

An emission snapshot for a sub‐ region

How do we reduce emissions? Case study of flares

• Flares have narrow
• perating windows

to achieve both high combustion and low soot

• Full scale field

studies to define those operating windows and then train operators

Mitigating the impact of super‐emitters: Improvements 2000‐2006

Data from NOAA

Applying these approaches to the Eagle Ford

Quantify the emissions from the Eagle Ford

• While emissions of

volatile organic compounds are the largest in quantity, they are relatively unreactive, and in the Eagle Ford occur in a region with high emissions of biogenics

• Ozone formation in the

Eagle Ford is due almost exclusively to NOx

NOx VOC CO Eagle Ford Base Case Emissions Inventory (tpd) 50 100 150 200 250 300 350

Exploration Pad Construction Drilling Hydraulic Fracturing Well Completion Routine Production Midstream

50 100 150 200 250 300 350

Exploration Pad Construction Drilling Hydraulic Fracturing Well Completion Routine Production Midstream

Where the emissions occur also matters

NOx emissions that impact San Antonio NOx emissions that impact Austin

What is the magnitude of the impact?

• Magnitude of impact

approximately 1 ppb in Austin and San Antonio, depending on day and location; effects are likely to be stochastic

Summary (Findings)

• How we address emissions along oil and gas

supply chains is one of the most significant issues at the intersection of energy and air quality for the nation and the world

• Air emissions in the oil and gas supply chain are

changing and have complex spatial and temporal variability

• Addressing super-emitters is a significant part of

this challenge

• Experience over the past decade provides

information about what will and will not work

Recommendations

• Quantifying super‐emitters

How can we develop emission inventories accounting for super‐emitters?

EFi, super-emitter * (f) AFi, + EFi, non-super-emitter * (1-f) AFi, = ERi

EFi, super-emitter = Emission Factor for super-emitters in region i EFi, non-super-emitter = Emission Factor for non-super-emitters in region i AFi = Activity Factor for region i f = Fraction of the Activity Factor attributed to super-emitters ERi = resulting Emission Rate total for region i

What do we need to be measuring if the goal is to estimate total emissions?

EFi, super-emitter * (f) AFi, + EFi, non-super-emitter * (1-f) AFi, = ERi

EFi, super-emitter = Emission Factor for super-emitters in region i EFi, non-super-emitter = Emission Factor for non-super-emitters in region i AFi = Activity Factor for region i f = Fraction of the Activity Factor attributed to super-emitters ERi = resulting Emission Rate total for region i

What do we need to measure if the goal is to reduce emissions to a target level?

EFi, super-emitter * (f) AFi, + EFi, non-super-emitter * (1-f) AFi, = ERi

EFi, super-emitter = Emission Factor for super-emitters in region i EFi, non-super-emitter = Emission Factor for non-super-emitters in region i AFi = Activity Factor for region i f = Fraction of the Activity Factor attributed to super-emitters ERi = resulting Emission Rate total for region i

Recommendations

• Quantifying super‐emitters
• Develop consistency in reporting emissions

Challenges in reporting emissions

• Different portions of the supply chain

– Some studies examine all parts of the supply chain in a region, some focus on specific sources

• Different spatial scales

– Some national estimates, some regional estimates, but significant regional differences have been observed

• Different temporal scales

– Some instantaneous measurements, some annual averages

• Inconsistent reporting metrics

– Tendency to focus on fraction of natural gas used or produced that is emitted, but numerator and denominator in this fraction should be defined precisely and consistently

Recommendations

• Addressing super‐emitters

– Need technologies to rapidly find and fix

• Develop consistency in reporting emissions
• More top‐down and bottom‐up studies are

needed

• Reconciliations between top‐down and bottom‐

up studies need to recognize spatial variability, temporal variability and role of super‐emitters

• More information needed on air toxics
• More global information needed