# Final Report

For Determining Bleed Rates for Pneumatic Devices in British Columbia

December 18, 2013i | P a g e



# Executive Summary

Regulations in BC stipulate that all greenhouse gas (GHG) emissions from pneumatic instruments and pumps must be
tracked for reporting and compliance purposes. In order to help industry quantify these emissions more efficiently and cost effectively, the BC Climate Action Secretariat (CAS) , Ministry of Natural Gas Development and Canadian Association of Petroleum Producers ( CAPP) engaged The Prasino Group (Prasino) to determine bleed rates for a suite of common pneumatic controllers and pumps. This survey is the first of its kind and was funded by the Science and Community Knowledge (SCEK) Fund.

Pneumatic controllers and pump s use pressurized fuel gas to perform operations such as pressure control, temperature control, liquid level controller and chemical injection. This fuel gas is subsequently released to the atmosphere after the operation is performed. The bleed rate of a p neumatic device is defined as the amount of fuel gas released to the atmosphere per hour. Figure 1 shows the breakdown of GHG emissions from the oil and gas sector in BC with a detailed
breakdown of the sources of vented met hane. The high bleed pneumatic controllers and pumps, which are the subject of this study , contribute 436 ,000 tCO2e of the 1,723,000 tCO2e from vented methane. The amount of contribution from pneumatic devices and pumps is expected to change as a result of this study.

Figure 1 : Detailed Breakdown of vented and fugitive methane sources in the BC oil and gas sector (source BC CAS 2013).

T he purpose of the survey was to determine the average bleed rate of pneumatic controllers and pumps when operating
under field conditions in BC. Bleed rates were sampled from pneumatic devices using a positive displacement bellows meter at upstream oil and gas facilities across a variety of producing fields in the Fort St. John, BC and surrounding, areas

. Descriptive statistics, general linear models and regression analysis was performed on the data to investigate the bleed rates and draw robust, relevant conclusions.

All outcomes were achieved. The results of the analysis led to the development of three generic bleed rates and tw enty specific bleed rates for common pneumatic controllers and pumps for BC’s oil and gas industry. These bleed rates can be used in the development of emission factors for GHG reporting and potentially offset purposes.ii | P a g e

Table 1 : Su mmary of Findings

P neumatic Device Average Bleed Rate (m 3 /hr) Coefficients 1 (supply pressure,

injection pressure, strokes per min) Equivalent Device

Generic High Bleed Controller 0.2605 0.0012

- Generic High Bleed Intermittent Controller 0.2476 0.0012

-

Pressure Controllers

Fisher 4150 0.4209 0.0019 4150K, 4150R, 4160, CVS 4150

Fisher C1 0.0649 0.0003

-
Fisher 4660 0.0151

- 4660A

Level Controllers

Fisher 2500 0.3967 0.0011 2500S, 2503, L3

Fisher 2680 0.2679 0.0014 2680A

Fisher 2900 0.1447

- 2900A, 2901, 2901A

Fisher L2 0.2641 0.0012

- Murphy L 1200 0.2619 0.0012 L1100, L1200N, L 1200DVO

Norriseal 1001 0.1868

- 1001A, 1001XL

SOR 1530 0.0531

- -
Positioners

Fisher Fieldvue DVC6000 0.2649 0.0011 6030, 6020, 6010
Temperature Controllers
Kimray HT

- 12 0.0351

- - Transducers

Fairchild TXI 78 00 0.1543 0.0009 TXI7850

Fisher 546 0.3547 0.0017 546S

Fisher i2P

- 100 0.2157 0.0009

- Pumps
Generic Piston Pump 0.5917 0.00202, 0.000059, 0.0167

- Generic Diaphragm Pump 1.0542 0.0005, 0.000027, 0.0091

-

Mo rgan HD312 1.1292 0.00418, 0.000034, 0.0073 HD312

- 3K, HD312

- 5K

Texsteam 5100 0.9670 0.0003,0.000034, 0.0207 5100LP, 5100H

Williams P125 0.4098 0.00019, 0.000024, 0.0076

-
Williams P250 0.8022 0.00096, 0.000042, 0.0079

-
Williams P500 0.6969 0.00224,

- 0 .000031, 0.0046

-

1 Controllers that do not have a coefficient should use the mean bleed rate instead of the bleed rate equation.iii | P a g e



# Table of Contents

Executive Summary

................................ ................................ ................................ ................................ ........................ i

Table of Contents

................................ ................................ ................................ ................................ .......................... iii

1. Introduction

................................ ................................ ................................ ................................ .......................... 1

2. Sampling Methodology

................................ ................................ ................................ ................................ ......... 1
2.1 Pneumatic Controllers

................................ ................................ ................................ ................................ ................. 2 2.2 Pneumatic Pumps

................................ ................................ ................................ ................................ ........................ 4 2.3 Sampling Approach

................................ ................................ ................................ ................................ ...................... 4 2.3.1 Bleed Rate Metering

................................ ................................ ................................ ................................ ............ 5 2.3.2 Sample Size

................................ ................................ ................................ ................................ ........................... 7
2.3.3 Data Collection and Transfer

................................ ................................ ................................ ................................ 7 2.3.4 Errors, Uncertainty and Biases

................................ ................................ ................................ ............................. 8

3. Sample Characteristics

................................ ................................ ................................ ................................ ........ 11
3.1 Pneumatic Controllers

................................ ................................ ................................ ................................ ............... 11 3.2 Pneumatic Pumps

................................ ................................ ................................ ................................ ...................... 12

3.3 Producers

................................ ................................ ................................ ................................ ................................ ... 12

3.4 District and Sub

- District

................................ ................................ ................................ ................................ ............. 12

4. Analysis

................................ ................................ ................................ ................................ ............................... 14 4.1 Analysis for Determining a Generic Bleed Rate

................................ ................................ ................................ ......... 15 4.2 Bleed Rate Equations

................................ ................................ ................................ ................................ ................. 16

5. Comparison of Bleed Rates

................................ ................................ ................................ ................................ .. 18

6. Discussion

................................ ................................ ................................ ................................ ............................ 19

6.1 Outliers

................................ ................................ ................................ ................................ ................................ ...... 19 6.2 Throttling vs. Snap

- Acting Controllers

................................ ................................ ................................ ....................... 19 6.3 Manufacturer Specification

................................ ................................ ................................ ................................ ....... 20 6.4 Gas vs. Air vs. Propane

................................ ................................ ................................ ................................ .............. 20

6.5 Calscan Vent Gas Meter

................................ ................................ ................................ ................................ ............ 21

6.6 Producer and Sub

- District

................................ ................................ ................................ ................................ .......... 21 6.7 Adding a Device Model to the Survey

................................ ................................ ................................ ....................... 2 1

6.8 Mean vs. Median

................................ ................................ ................................ ................................ ....................... 21

6.9 General Linear Model

................................ ................................ ................................ ................................ ................ 22 6.10 Summer vs. Winter Sampling

................................ ................................ ................................ ................................ .. 22

7. Applications of the Analysis

................................ ................................ ................................ ................................ . 22

8. Observations

................................ ................................ ................................ ................................ ....................... 23

9. Conclusion

................................ ................................ ................................ ................................ ........................... 24
References

................................ ................................ ................................ ................................ ................................ ... 24 Appendix A: Compilation of All Known Pneumatic Controllers

................................ ................................ .................... 27 Appendix B: Initial List of Pneumatic Devices Included in the Sample

................................ ................................ .......... 32 Appendix C: Field Sampling Guide

................................ ................................ ................................ ................................ 34 Appendix D: Box Plot Distributions

................................ ................................ ................................ .............................. 35 Appendix E: Bleed Rate Equation Coefficients

................................ ................................ ................................ ............. 371 | P a g e



# 1. Introduction

This report outlin es the methodology and analytical methods used to develop bleed rates for reporting GHG emissions
from pneumatic controllers and pumps (collectively referred to as ‘devices’) in British Columbia (BC) . The development
of emissions factors may allow for an alternative method of monitoring and reporting GHGs from pneumatic devices, as per an agreement between the Canadian Association of Petroleum Producers (CAPP) and the B.C. Ministry of Environment’s Climate Action Secretariat (CAS)

. The Prasino Group (Pras ino ) has been engaged by the Science and Community Environmental Knowledge Fund ( SCEK) in order to develop these bleed rates based on quantitative sampl ing of pneumatic devices in BC. This document is the final project report and builds off the subsequent three reports (Sampling Methodology, First Round Sampling Report and Final Sampling Report) that were submitted to SCEK. This document is meant to be a standalone report and describes:

 The sampling methodology used to obtain th e field bleed rate samples;

 T he characteristics of the samples ;

 The statistical analysis performed on the field bleed rate samples as well as a discussion of the results; and

 The recommended bleed rates for each pneumatic device included in the survey.



# 2. Sampling Methodology

Pneumatic devices used in B.C.’s oil and gas sector fall into two categories:

1. Pneumatic chemical injection pumps ( typically injecting methanol into a pipeline); and

, 2. Pneumatic controllers, which regulate pressure, temperature, fluid level, or some other process vari able

.

There are dozens of manufacturers of the types of pneumatic devices listed above. Even though the device types perform similar functions, they have inherently different bleed rates. Due to constraints, it was necessary to narrow sampling to
the most common or representative devices. A “Device Selection Approach” was used to narrow the sample and
determine the most common devices in the field.

Figure 2 below outlines the steps that were followed to develop the initial list of devices for sampling. Further detail will be provided in the next section. The following section describes the process that was used for selecting which devices to include in the sampl ing regime

.2 | P a g e

Figure 2 : Device Selection Approach 2



### 2 .1 Pneumatic Controllers

In order to determine which pneumatic controllers to include in our sample, multiple steps were undertaken as illustrated
in

Figure 2 . Through this process, 15 contro llers were identified as common. For the rare devices that are not within the scope of this project, a generic high

- bleed rate has been developed for all high

- bleed controllers. This ensures that any pneumatic controller can apply a bleed rate that is repr esentative of field conditions.

Step 1: Compile All Known Controllers

Prasino initially developed a complete list of all known pneumatic low and high

- bleed controllers that were anticipated
to be used in the upstream oil and gas industry in BC. Using one or more of the sources listed below, the make, model,
and manufacturers’ stated bleed rate of each controller was determined:

 C anadian Environmental Technology Advancement Corporation

- West (C ETAC ) : Efficient Use of Fuel Gas in Chemical Injection Pumps. F uel Gas Best Management Practices. The BMP lists manufacturer bleed rates of controllers in m 3 /hr of natural gas.

 Pacific Carbon Trust (PCT): High

- Bleed to Low

- Bleed Conversion for Pneumatic Controllers. Meta

- Protocol for Oil and Gas Emission Reductions Pr ojects. In the protocol, the bleed rates were stated in standard cubic feet per hour ( scfh ) of air, based on manufacturer stated specifications. The volume of air bled was converted to natural gas by multiplying by 1.3 3 . When a range of bleed rates was lis ted, the highest val ue was taken to be conservative

.

 Environmental Protection Agency: Gas STAR

– Options for Reducing Methane Emissions from Pneumatic Devices in the Natural Gas Industry . This document stated the bleed rates of high

- and low

- bleed controll ers in scfh of
air. These values were converted to m 3 /hr of natural gas.

 Western Climate Initiative (WCI): Final Essential Requirements of Mandatory Reporting. This report references the BMP, PCT Protocol and EPA Gas STAR for the pneumatic controller list and bleed rates. The manufacturer
bleed rates in this document are in m 3 /hr.

 Manufacturer websites were referenced to determine the steady state air consumption for pneumatic controllers. The highest steady state air consumption was recorded. The bleed ra tes were stated in m 3 /hr and
scfh

.

 Cap

- Op Energy samples from the DEEPP database were used to look at controllers and pumps that are already in the field and have been sampled previously by GreenPath Energy Ltd 4

.
Step 2: Equivalent Devices

Controllers ma y have different make s and models but serve the same function . Controllers are considered equivalent devices if they have interchangeable parts

. A list of equivalent devices was compiled using information from device vendors and subject matter experts, and is presented in Appendix A (J. Anhalt, personal communication, July 2013; B. Van Vliet, personal communication, July 2013)

.

2 “ Cap

- Op’s Database” refers to Cap

- Op Energy’s Distributed Energy Efficiency Project Platform (D EEPP), which was queried for historical pneumatic controller information. Cap

- Op Ener gy is a sub

- contractor to Prasino

.

3 The value 1.3 is based on the density and molar mass of air and natural gas in ideal gas conditions. This manner of conversion is an industry standard.

4 GreenPath Energy Ltd . is the contractor who was responsible for completing the field sampling protocol.3 | P a g e

Step 3: Ensure Manufacture r Bleed Rates are G reater than 0.119 m 3 /hr

Manufacturer bleed rates were used to determine which controll ers are considered high bleed and therefore relevant for sampling. However, these manufacturer bleed rates are based on manufacturer lab testing and may not reflect ac tual field conditions. The steady state bleed rates reported are static bleed rates of co ntrollers that are not actuating and thus dynamic bleeding is not captured . Therefore, the manufacturer bleed rates may not accurately express the actual vented natural gas through these controllers because the steady st ate does not include dynamic bleedin g. The relationship between the bleed rates of controllers that are running on dirty/wet natural gas compared to air is unknown. It is likely that controllers operating in the field bleed more than controllers tested in a laboratory using air.

The current definition whether a controller is a high or low bleed controller is based on the WCI Reporting Regulation definition: “high

- bleed devices are defined as all natural gas powered devices which continuously bleed at a rate greater
than 0.17 m 3 /hr.”

Many con trollers have manufacturer bleed rates just below 0.17 m 3 /hr, and thus appear to be a low

- bleed controller. Since manufacturers do not consider the dynamic bleed rate in their stated bleed rate , many low

- bleed controllers in fact
bleed more than 0.17 m 3 /hr on a regular basis. To ensure all relevant controllers that bleed more than 0.17 m 3 /hr (including static and dynamic bleeding) are included within the sample, the manufacturer bleed rates were compared to
a limit of 0.119 m 3 /hr (CAS, 2013)

. In many cases, the manufacturer states a range of bleed rates that are dependent on other operating parameters of the controller (i.e. 1.4 scfh at 20 psi vs. 3 scfh at 30 psi). In this case, the highest bleed rate was recorded to ensure that all controllers with the pot ential to bleed higher than 0.17 m 3 /hr were included. Refer to Appendix A for highest manufacturer bleed rates. Controllers that have been excluded from sampling as a result of this step are represented at the bottom of the table in Appendix A.

Step 4: Q uery Subject Matter Experts

Subject matter experts were queried as per the request of CAS to determine if the list of pneumatic devices was inclusive and representative . Several low

- bleed controllers below the limit of 0.119 m 3 /hr have been included based on these
discussions to investigate if controllers that are labelled according to manufacturer specification actually perform as a low
bleed device in the field. Four low

- bleed controllers were included in the survey: Fisher C1, Fisher 4660, SOR 1530 and

K imray HT

- 12. The results of the query are represented in Appendix A.

Step 5: Determine the Frequency of O ccurrence of Controllers

Using Cap

- Op’s DEEPP database, t he eligible list of all pneumatic c ontrollers was filtered down to focus sampling on
device s that are considered common. Cap

- Op Energy has an extensive +2000 sample database from previous work on pneumatic devices from the upstream oil and gas industry in Alberta and BC. This database was used as a proxy for determining which controllers are com mon among producers in the Canadian oil and gas industry. The samples could not however be included in the survey because they were taken using the Bacharach High

- Flow Sampler and this survey is using the Calscan vent g as bellows meter for reasons presente d in Section 2.4.3. These eligible controllers were compared
with the extensive field samples database from Cap

- Op Energy’s DEEPP to examine the frequency of eligible controllers previously surveyed in the field. The results are depicted in Figure 3 below.

Step 6: Develop Initial List

The common list of pneumatic controllers used to guide first round sampling can be found in Appendix B. The results
include:

 The top 10 controllers represent 89% of the surveyed contr ollers in the Cap

- Op database.

 The top 15 controllers represent 97% of the surveyed controllers in the Cap

- Op database.

 Rare devices are those that comprise the remaining 3% of the surveyed controllers in the Cap

- Op database.4 | P a g e

This initial list was use d to guide first round of sampling because the selected devices were anticipated to be frequent enough to produce statistical ly valid emissions factors.

Step 7: Validate list

Upon completion of the first round o f sampling the original list was compared wi th what was observed in the field to determine if the anticipated list of 15 devices was the most common. Based on survey data collected in the field, two devices were found to be common and added to the sample: Fairchild TXI 780 0 ; and Murphy L1200. Two de vices were found to be rarer than initially thought and thus have been removed from the sample population: Fisher 2660 (no devices found in the field); and Dyna

- Flo 4000 (two devices found in the field).

Figure 3 : Frequency of p neumatic controllers found in the Cap

- Op database



### 2 .2 Pneumatic Pumps

The methodology used for determining the list of pneumatic pumps does not mirror the methodology used for pneumatic controllers because the Cap

- Op database does not contain sufficient pu mp field samples to draw similar conclusions. The list of pneumatic pumps was compiled from the CAPP (2008) , PCT (2011) and the Cap

- Op DEEPP database. These sources

were cross

- referenced with manufacturer websites and subject matter experts to make a comp rehensive initial list

, presented in Appendix B. This initial list was used to guide first round of sampling ; however, all pumps were sampled in the first round to determine which types were common among the producers sampled. Five common pumps were identi fied aft er the first round of sampling and were targeted during the second round of sampling : Texsteam 5100; Morgan HD 312; Williams P125; Williams P250; Williams P500.



### 2.3 Sampling Approach

The following section provides detail on how the field sample s we re colle cted as well as justification for which companies and geographic areas were selected for sampling

. The analysis and discussions in this report distinguish the ‘sample set’ or ‘sample’ from the ‘population’. In general, this analysis operates under the assumptions that the true number and state of the population (i.e. all pneumatic devices in BC) is unknown. Determining whether the sample is representative of the population, and to what extent is a major Fisher 4150 26%

Fisher i2P

- 100 12%

Norriseal 1001 12% Fisher
2900 11% Fisher 546 7% Fisher L2 6% Fisher 2680 5% Fisher 4660 5% Fisher Fieldvue
DVC6000 3% Kimray HT

- 12

2% Fisher 2660 2% Fisher

2500 2% SOR 1530 2% Fisher C1

1% Norriseal 1001XL

1% Other (56

controllers) 3%5 | P a g e

component of this report and helps to charact erize the validity of the results. Statistical analyses and conclusions are contingent on the assumption of an unknown population.



### 2.3 .1 Bleed Rate Metering

The metering requirements for this project are unique and as such there are few options for dete rmining gas vent rates
from devices in a cost effective manner.

The desired meter must have the following characteristics:

 Mobility

– the meter will have to be mobile such that the sampling teams can apply the meter to pneumatic
devices in

- service at var ious facilities throughout BC. This requires a relatively simple installation and removal procedure, with a low weight and portability such that it can be moved by one or two people to within a few feet of an existing pneumatic device as it is installed on

- site. Breaking, cutting or dissembling process pipes, flanges or joints is not acceptable, however vent tubing may be intercepted for measurement.

 Range

– the meter must be designed to accommodate the measurement of flow rates between zero and approximat ely 1m 3 /h of gas at pressures ranging from atmospheric to a few feet of water column with minimal back pressure.

 Explosive Environment

– All electronic devices intended for use within CSA classified zones require intrinsically
safe electronic enclosures s uch that potentially arcing /sparking ignition sources are completely isolated from potentially explosive atmospheres that occur in oil and gas facilities. This is a minimum safety requirement for all sampling team and on

- site personnel.

The desired meter should have the following characteristics:

 Time series data to distinguish static and dynamic operation on pneumatic devices.

 Minimal to no back pressure

 High precision, 2% or less uncertainty on reported values

 High accuracy, with correction for variab le parameters such as gas composition, temperature, barometric pressure, humidity, etc.6 | P a g e

Based on an extensive review of meters available in the industry, two options which
were found to be best candidates included the Bac h arach High Flow Sampler and the
Calscan Hawk 9000 Vent Gas Meter. The Bac h arach High Flow Sampler is designed to measure the rate of gas leakage around various processes in natural gas transmission, storage and compressor facilities. This is accomplished by sampling at a very large flow rate (8 to 10 scfh) to completely capture any gas leaking from the component. By accurately measuring the flow rate of the sampling stream and the natural gas concentration, it is possible to calculate the rate of gas leak.

Prasino elected to use the Hawk 9000 vent gas meter ( supplied by Calscan Energy) to measure and digitally log flow vent gas over time (which will vary based on the device sampled) . This allowed for both the static and dynamic bleed rates for pneumatic
controllers

, as well as the dump c ycles for pneumatic pumps and level contr ollers, to be captured. A drawback of the Bacharach High Flow Sampler is that it captures a snapshot in time; rather than a time series measurement. The Bacharach High Flow Sampler can fail to capture the dynamic bl eeding events. The Hawk 9000 meter uses a positive displacement diaphragm meter that detects flow rates down to zero

, and can also effectively measure any type of vent gas (methane, air, or propane). In addition, the Hawk uses a precision pressure sensor, an external temperature probe and industry standard gas flow measurement algorithms to accurately measure the
gas rates and correct for pressure and temperature differences . As a result, flow
measurement accuracies within ± 2% 5

. A picture of the Calscan Haw k 9000 is presented in Figure 3 and an example output chart is provided in Figure 5 below.

5 The meter is calibrated from

- 40°C to +60°C and uses “Gas Rate Algorithm AGA7” and “Equation of State AGA8”. Figure 4 : Calscan Hawk 9000 Meter7 | P a g e

Figure 5 : Example of Calscan Output Graph



### 2 .3 .2 Sample Size

The sampling program aimed to collect thirty sampl es of each device type identified as common according to Section 2.0 Sampling Methodology . Thirty was chosen as a minimum sam ple size in order to allow for statistica l inferences to be
drawn . When the sample size is sufficiently large (conventionally, 30 or larger), the standard error can be used to calculate

a one

- tail 95% confidence interval. As the sample population increases, the confidence interval should get smaller due to
a decreased standard error (McClave and Sincich, 2003)

. In general, when calc ulating confidence interval, larger sample
size s and narrow confidence intervals must be balanced against the cost of additional sampling and the diminishing returns of incrementally smaller improvements to confidence intervals with each additional sample

. Thirty samples for each common device type allows for the quantification of bleed rates with confidence intervals within a realistic budget
and time frame.



### 2 .3 .3 Data Collection and Transfer

To manage the large amount of data that was collected during t his sampling program , Cap

- Op designed a software application (app) to be used in the field in order to increase data quality and tracking

, and eliminat e manual data recording. A field sampling guide was followed by the Greenpath sampling team and is provid ed in Appendix C.

All parameters were inputted into the app at the sampling location

. Where appropriate, the app has dropdown menus to increase efficiency in compiling data . Numeric input fields have expected ranges of values and options for the units, so that if a value is entered outside of the range a message appears for the user to ensure the input is correct.

When the user has access to internet, the app w ill sync with Cap

- Op Energy ’s DEEPP . The DEEPP will provide various functionalities for managi ng the data co llected in the field including d ata storage and o rgani zing the data into the desired output format of a download

- able Excel files.8 | P a g e

Figure 6 : Screen Shot of Cap

- Op Energy's Data Collection App



### 2 .3 .4 Errors , Uncerta inty and Biases

Errors, uncertainty and biases are part of every analysis and are discussed here for transparency and clarity. Overall, through critical review of the potential errors, uncertainties and biases inherent to this study, robust and reliable re sults
can be attained.

An error is an unsupported result or conclusion that arose through improper application of methods, calculations, or data management. Every effort has been made to eliminate errors from the analysis and a system of checks has been e mployed to eliminate error from the analysis. Error can be eliminated from analysis, while bias and uncertainty cannot be.

Uncertainty , as it pertains to measurement of physical states or processes, describes the precision (or lack thereof) at
which a cha racteristic or parameter can be defined. The precision associated with the measurement of bleed rates from a pneumatic device can be characterized through review of the techniques used to measure the parameters in question.9 | P a g e

The meter chosen for this projec t ( see Section 2.3 .1) uses self

- actuating, reciprocating bellows to discretely count fixed volumes of gas being vented from the device in five second intervals . The limitations associated with this type of measurement, and the uncertainties imposed, includ e:

 Volumes of gas vented lower than the fixed volume of one inflated bellow in a five second interval cannot be resolved. The uncertainty of any values of bleed rate reported by the meter under this value is large. This effect is typically characterized a s the ‘turn

- down ratio’ of a meter and every meter will have limits, turn

- down or some range beyond which its reported values are unreliable. The Hawk 9000 meter has t he range of flows that is appropriate for the intent of this study and thus mitigates thi s form of uncertainty to the extent possible.

 Pressures of vented gas lower than the back pressure imposed by the meter will significantly alter the value of gas being vented by the device. Back

- pressure is a significant concern when measuring pneumatic co ntrol devices since the performance and bleed rate of the device may be inherently dependent on the back pressure of vented gas. This uncertainty impacts all ranges of gas flow, but in general will impose large uncertainty on high flows of gas since back p ressure increases with flow. Devices which reported a zero bleed rate may actually have simply been ‘plugged’ by the internal actuating mechanisms of the meter. In general, it is impossible to measure the state of a system without disturbing the system, an d this effect is unavoidable regardless of the type or extent of metering equipment chosen. It is anticipated that while the metering system chosen does impose a back pressure which may be more significant than other metering systems, the benefits of this metering system (time series bleed rate values and temperature pressure compensation) outweigh the detrimental effects of back pressure on the validity of results.

 Gas compositions vary from location to location and from moment to moment and the estimatio n of gas composition at each measurement point is a source of uncertainty. It is prohibitively expensive to have real

- time gas composition parameters available to the measurement devices such that the uncertainty associated with gas composition could be fu rther reduced. The estimations and assumptions made on gas composition for temperature and pressure correction of measured volumes is considered appropriate for the scope of this project.

 Digitization of data imposes uncertainty to values recorded during measurement through the limitation of significant digits (decimal places). The uncertainty imposed through the significant digits carried by data management systems in this project is considered to have contributed a negligible degree of uncertainty to the
overall result.

In general, a statistic is biased if it is determined by an approach or method which systematically gives rise to differences between sample data and population data. Every effort was made in this sampling approach to avoid or minimize th e effects of biases, nevertheless biases exist and are discussed below:

 Opportunistic Sampling Approach

- The sampling approach used is a non

- probability technique called opportunistic sampling

, w here sampling locations were chosen purposefully

. The locat ions were chosen based on the following criteria:

o The proximity to Fort St. John

. Fort St. John is arguably a hub of oil and g a s production within BC , with a majority of activity found within 500 km. In order to determine device bleed rates in an efficient and

cost

- effective manner, sampling was focused in this area. Sampling did not occur in the Fort Nelson region because many sites in Northern BC are winter access only or only accessible by helicopter.

o The accessibility due to seasonality. Field locations with winter access only were excluded from the survey due to logistics and cost

.

o Producer identified device “hot spots”. Areas with a high concentration of devices were identified by

cross

- referencing producer’s inventories with the list of common devices

.1 0 | P a g e o Sweet n atura l gas well

- sites were preferred, followed by compressor stations and batteries , then sour gas well sites

. Sweet sites were preferred over sour sites because they are typically run off compressed natural gas, whereas sour sites are typically r un off propane or air. Well

- sites were preferred over larger facilities because they typically house the common pneumatic controllers and pumps.

Opportunistic sampling has known limitations

, including the sampling error 6 which cannot be estimated, and tha t exclusion bias may arise from the non

- random choice of sampling locations. However, random sampling was logistically impractical in this scenario and all efforts to minimize exclusion bias were made, by choosing sampling
locations that were representati ve of a multitude of producers operating in BC, and production fields and sub

districts.

The effect of this exclusion bias is that the statistics describing the sample

, may not accurately reflect the population statistics, and the extent to which this occ urs cannot be determined. The likelihood and impact of this bias has been minimized by ensuring a sufficiently large sample size (the greater the sample size, the less
selection biases will impact a result).

 Producer Influence

– The sampling approach desc ribed above inherently allows for a set of biases which are the possibility for intentional and unintentional skewed sampling. Since permission was required from producers to access sites, and moreover, recommendations on which sites to visit were solicite d from producers, it must be acknowledged that the producers may have unintentionally directed sampling to areas with more or less bleeding devices.

 Location

– Pneumatic devices in the province of BC are limited to operation in the northern boreal shield or boreal plains climate s at altitudes approximately centered around 690m above sea level. Oil and gas reserves are limited to those found in the western Canadian sedimentary basin and are not representative of global conditions. Biases arise from these ge ographic limitations and apply should the results of this study be implicated in jurisdictions outside of BC, however are irrelevant within the context of BC pneumatic device emissions.

6 Sampling error is an estimation of the difference between the true population mean and the sample mean, usually expressed in terms of standard error. Standard error cannot be reliably calculated using non

- probability sampling techniques, although a mean, standard deviatio n, and confidence interval can be calculated with large (>30) sample numbers.11 | P a g e



# 3. Sample Characteristics

In order to calculate a statistically signif icant bleed rate, with 95% confid ence, a minimum of 30 samples was required per
device. A total of 765 samples were taken acros s 28 producing fields in BC and 2 producing fields in Alberta

.

Table 2 . Number of Samples by Device Type

D evice Type Number of Samples

Pneumatic Controllers Level Controller 254

Positioner 43

Pressure Controller 142
Temperature Controller 41

Transducer 101
Pneumatic Pumps Chemical Injection 184



### 3.1 Pneumatic Controllers

Table 3 (below) summarises the number of samples by controller device. Devices in t he “other” category were used to develop a generic emissio ns factor for pneumatic devices not specifically listed here

.

Table 3 . Pneumat ic Controllers Sampled

P neumatic Controllers First Round Samples Second Round Samples Total

Pressure Controllers

Fisher 4150 35 11 46

Fisher C1 27 3 30

Fisher 4660 29 1 30

Level Controllers

Fisher L2 37 11 48
Murphy L1200 27 4 31

Norriseal 1001 47 10 57

SOR 153 0 28 3 31

Fisher 2900 22 8 30

Fisher 2680 22 10 32

Fisher 2500 8 4 12

Positioner
Fisher Fieldvue (DVC) 20 12 32
Temperature Controller
Kimray HT

- 12 36 0 36

Transducer

Fisher i2P

- 100 37 0 37

Fisher 546 27 3 30

Fairchild TXI7800 36 1 37

Other 53 7 64

Total 491 90 58112 | P a g e



### 3. 2 Pneumatic Pumps

The sampling results for pump devices are summarised in Table 4 (below).

Table 4 . Pneumatic Pumps Sampled

P neumatic Pumps First Round Samples Second Round Samples Total

Morgan HD312 3 32 35

Texsteam 5100 47 0 47

Williams P125 50 0 50

Williams P250 28 0 28

Williams P500 12 0 12

Other 9 3 12
Total 149 35 184



### 3.3 Producers

To reduce sampling bias, a cross

- section of oil and gas producing companies were include d in the survey to ensure sampling was representative and spread across producers as well as producing fields. Figure 3 (below) shows the breakdown of sampling across the eight producers.

Figure 7 : Breakdown of Samples by Produ cer



### 3.4 District and Sub

- District

Tabl e 5 o utlines the number of samples per district as well as a breakdown of samples by producing field. Samples were
collected from areas in north eastern BC ; in the Fort St John, Bro oks, Dawson Creek , Gra nd Prairie and Hanna districts ( Figure 8 ). In total samples were taken fro m 30 different producing fields, with 756 samples coming from BC and 9 from
Alberta.13 | P a g e

Figure 8 : Sampling Location

Tabl e 5 . Number of Samples by District a nd Sub

- District

P roducing Field Number of Samples

Dawson Creek 254

Bissette 111
Brassey 7

Half Moon 7

Redwillow River 41

Sundown 25

Swan Lake 63

Fort St. John 394

Beaverdam 5
Blueberry 4 2

Buick Creek 29

Bullmoose 4

Bulrush 11

Burnt River 42

Cecil Lake 27
Eagle 36

Farrell 9

Farrell Creek West 43
Ladyfern 14

Monais 4

Muskrat 33
Nancy 26

North Cache 714 | P a g e P roducing Field Number of Samples

North Pine 5

Owl 1
Septimus 16

Stoddart 29

Sukunka 11

Grand Prairie 7 10 8
Hiding Creek 45

Noel 63 Hanna (AB) 7

Leo 7 Brooks (AB) 2
Verger 2

Total 765



# 4. Analysis

The analytical approach began by determining the mean sample bleed rate for each pneumatic controller an d pump included in the survey. Table 6 below show the mean bleed rates, the 95% confidence interval (CI) and the standard deviation for the 20 common devices included in the survey. These bleeds rates are corrected for temperature, pressure and gas type.

Table 6 : Results of Analysis by Device Model

P neumatic Device Number of Samples Average Bleed Rate

(m 3 /hr) 95% Confidence Interval (m 3 /hr) Standard Deviation (m 3 /hr)

Pressure Controllers

Fisher 4150 46 0.4209 0.5322 0.4593

Fisher C1 30 0.0649 0.0981 0.1106

Fis her 4660 30 0.0151 0.0329 0.0592

Level Controllers

Fisher 2500 12 0.3967 0.5559 0.3353

Fisher 2680 32 0.2679 0.3782 0.3793

Fisher 2900 30 0.1447 0.2496 0.3490

Fisher L2 48 0.2641 0.3538 0.3779
Murphy L 1200 31 0.2619 0.3618 0.3383

Norriseal 1001 57 0 .1868 0.2670 0.3679

SOR 1530 31 0.0531 0.0841 0.1049

Positioners

Fisher Fieldvue DVC6000 32 0.2649 0.3633 0.3386

Temperature Controllers

7 Samples labelled Grand Prairie were taken from producing fields in BC.15 | P a g e

P neumatic Device Number of Samples Average Bleed Rate

(m 3 /hr) 95% Confidence Interval (m 3 /hr) Standard Deviation (m 3 /hr)

Kimray HT

- 12 36 0.0351 0.0621 0.0987

Transducers

Fairchild TXI78 00 37 0.1543 0.1877 0.1234

Fisher 546 30 0.3547 0.4279 0.2436

Fisher i2P

- 100 37 0.2157 0.2602 0.1646

Pumps
Morgan HD312 35 1.1292 1.3592 0.8271

Texsteam 5100 47 0.9670 1.1467 0.7490

Williams P125 50 0.4098 0.5092 0.4272

Williams P250 28 0.8022 1.0156 0.6863

Williams P500 12 0.6969 0.9741 0.5836



### 4.1 Analysis for Determining a Generic Bleed Rate

The next step of the analysis was to determine if a generic bleed rate could be generated for high

- bleed controllers and pumps. Devices that were determined to be high bleeding (i.e. bleed rate>0.17m 3 /hr) were grouped together in the analysis. If the calculated mean bleed rate was larger than the threshold, the device was included in the analysis, and if the calculated mean bleed rate was smaller than the threshold, the device was excluded from the analysis for determining a generic bleed rate . Certain controllers that are considered low

- bleeding according to WCI or manufacturer specifications actually bled above the low bleed threshold and were therefore included in the analysis. Using Minitab, a statistic al analysis software, a general linear model (GLM) was performed on the data to determine if there was a significant
difference between the mean bleed rates of controllers and pumps. The results are presented in Table 7

.

Table 7 : Results of the Overview Analysis

P neumatic Device Number of Samples Average Bleed Rate

(m 3 /hr) 95% Confidence Interval (m 3 /hr) Standard Deviation (m 3 /hr) P

- Value
High Bleed Controllers 406 0.2605 0.2880 0.3371 0.129
High Bleed
In termittent 195 0.2 476 .2893 0.3537 0.738

Piston Pumps 96 0.5917 0.6926 0.6007 0.060
Diaphragm Pumps 85 1.0542 1.1948 0.7878 0.362

For high bleed controllers, a p

- value>0.05 was calculated, meaning that there was no significant differences between the

m ean bleed rates. The mean bleed rate is representative of the population and can therefore be applied to any high bleed
controller model

. For intermittent high bleed controller, a p

- value>0.05 was calculated, meaning that there was no significant differenc es between the mean bleed rates. The mean bleed rate is representative of the population and can therefore be applied to any intermittent high bleed controller model. For all pumps, a p

- value< 0.05 was calculated

, meaning that there is a significant differe nce between all pump models and a generic bleed rate may not be representative of the entire population. Due to the large variance in bleed rates across all pumps, the pumps were grouped into two categories: diaphragm pumps and piston pumps

. A p

- value>0.05 was calculated for both types of pumps, meaning that the mean bleed rate is representative of the entire population.

Box plot distributions of all field sam ples are presented in Appendix D

.16 | P a g e



### 4 .2 Bleed Rate Equations

The most accurate bleed rate would tak e into account quantitative variables. A regression analysis was performed to investigate which quantitative variables affected the bleed rate. A regression analysis showed that there was a positive correlation between certain pneumatic controller bleed ra tes and supply pressure. A regression analysis showed that there was a positive correlation between pneumatic pump bleed rates, supply pressure, injection pressure and strokes per minute.

Controller Bleed Rate Equation Bleed Rate j = m ∗ SP

j Where:

m = the supply pressure coefficient (see Appendix E)

SP j = the supply pressure of controller j

Pump Bleed Rate Equation 8 Bleed Rate j = ( g ∗ SP

j ) + ( n ∗ DP

j ) + ( p ∗ SPM

j )
Where:

g = the supply pressure coefficient (see Appendix E)
SP j = the supply pressure of pump j, (kPa)

n = the discharge pressure coefficient, (see Appendix E)

DP j = the discharge pressure of pump j, (kPa)

p = the strokes per minute coefficient (see Appendix E)

SPM j = the strokes per minute of pump j

For producers who know the operating conditions of their devices, they should use the following bleed rate equations. It should be noted that this method will only provide a more accurate bleed rate compared to the average bleed rate shown in Table 7 and Table 8 if the producer is certain of the operating conditions. Adding complexity may increase the overall error in bleed rates if operating conditions are estimated. Figure 9 provides a n overview of the approach used to analyze
the data.

8 It should be noted that if the pump is operating at less than five strokes per minute, the emissions equation is not applicable and the mean bleed rate should be used.17 | P a g e

Figure 9 : Diagram Illustrating the Quantitative Analysis18 | P a g e



# 5. Comparison of Bleed Rates

Table 8 and Table 9 compare the average bleed rate to the manufacturer’s specification as well as previously published bleed rates. A discussion on variability between the mean bleed rate and the manufacturer specification is provided in
Section 6 below. A discu ssion on variability between the mean bleed rate and the EPA default is provided below. Overall, the average bleed rate for high bleed and intermittent controllers were lower than the EPA default; however, the average
bleed rate for low bleed controllers a nd pump s were both higher than the EPA default. These findings align with a similar study that was performed in the United States by the University of Texas (Allen et al. 2013).

Table 8 : Comparison of Pneumatic Controller Bleed Rat es

P neumatic Controllers Average Bleed Rate (m 3 /hr) Manufacturer Specification (m 3 /hr) WCI (m 3 /hr) CAPP (m 3 /hr) EPA (m 3 /hr)

Pressure Controllers

Fisher 4150 0.421 0.691 0.736 0.680

- 1.841 0.071

- 0.821
Fisher C1 9 0.06 5 0.097 0.147

- - Fisher 4660 0.015 0. 174 0.142

- 0.142
Level Controllers

Fisher L2 9 0.264 0.032 0.043

- - Fisher 2500 0.397 1.100 1.189 1.189 0.283

- 2.03

Fisher 2680 9 0.268 0.040 0.028 <0.028 <0.028

Fisher 2900 0.145 0.453 0.651 0.510

- 3.60

- Murphy L 1200 10 0.262

- - - -
Norriseal 1001 9 0 .187 0.057 0.057 0.006 0.006

SOR 1530 7 0.053 0.142

- - -
Positioners

Fisher DVC6000 0.265 0.38 0.396 0.400

- 1.39 0.396
Temperature Controller
Kimray HT

- 12 9 0.035 0.000

- - -
Transducer

Fairchild TXI78 00 0.154 0.380 0.241

- - Fisher 546 0.355 0.648 0 .850 0.423

- 1.700 0.595

Fisher i2P

- 100 0.216 0.180 0.283

- -

9 Considered a low bleed controller according to manufacturer specification and WCI’s definition <0.17m 3 /hr.

10 This pneumatic device was not on the initial list for pneumatics but was incl uded due to prominence in the field.19 | P a g e

Table 9 . Co mparison of pneumatic pump bleed rates.

P neumatic Pumps Mean Bleed Rate

(m 3 /hr) Max Air Consumption ( Man Specification 11 (m 3 /hr) CAPP (m 3 /hr) 12 WCI (m 3 /hr) 13 EP A (m 3 /hr)

Morgan HD312 1.1292 1.35 0.236 0.3945 0.3767

Texsteam 5100 0.9670 2.31 0.236 0.3945 0.3767

Williams P125 0.4098 0.21 0.236 0.3945 0.3767

Williams P250 0.8022 1.33 0.236 0.3945 0.3767

Williams P500 0.6969 2.46 0.236 0.3945 0.3767



# 6. Discussion

6 .1 Outliers

O utliers were not excluded from the sample population because the purpose of the survey was to capture real field conditions and generate a bleed rate that is representative of all field conditions. Certain controllers can have abnormally high bleed rates du e to operations and maintenance; however, these bleed rates are representative of real world
conditions and therefore were included in the analysis

.



### 6 .2 Throttling vs. Snap

- Acting Controllers

Two types of controllers were sampled in the fiel d, throttling and snap

- acting controllers. Throttling controllers bleed continuously as they constantly throttle between static and dynamic states. Actuating or intermittent bleed devices perform snap

- acting control and release gas only when they stroke a valve open or closed. The static bleed rate is steady

state gas consumption . When a controller performs an action, the pressurized gas is subsequently vented through the controller to the atmosphere, also known as the dynamic bleed rate.

The dynamic bleed rate can be much greater than the static bleed rate based on the operating conditions of the controller.
The total bleed rate (static + dynamic) depends on the frequency the cont roller is performing an action. Snap

- acting controllers typically have greate r variability in dynamic and static action due to the intermittency of the actions. Snap

acting controllers are predominantly in their static, inactive state until an action is required, which results in a short bu rst of dynamic bleeding.

Figure 10 shows an example of how the bleed rate varies over time for a snap

- acting controller. The difference between the static rate and the amplitude of the dynamic event is the dynamic bleed rate. The most important variable that
dictates the bleed rate however is the frequency of the dynamic events, which is dependent on a number of variables (dry/wet gas, tank size, etc.) Level controllers are a prime example of a snap

- acting controller because they only dynamically bleed when they are promp ted by an event, typically to empty a liquids tank.

11 The stated manufacturer max air consumption value assumes a supply pressure of 690 kPa (100 psi), which is a max supply pressure.

12 http://www.capp.ca/getdoc.aspx?DocId=86223&DT=NTV

13 http://www.theclimateregistry.org /downloads/2010/04/Final

- OGP

- Protocol.pdf20 | P a g e

Figure 10 : Graph Showing the Dynamic and Static Action

S ince the sampling methodology limited the sample time to 30 minutes, there was variability in the amount of static and
d ynamic action that was captured from a given controller. This variability was due to the frequency of dynamic events.

D epending on operating conditions, certain controllers did not perform an action within the 30 minute timeframe, so only
the static bleed rate was captured. For example, if a level con troller dumps on average every four hours, the sampling team may or may not have caught the dynamic bleed rate of the controller. This created variability in the amount of dynamic versus static bleeds that were capture by an individual sample; however, due to our large sample sets, this variability is representative of how controllers are performing under real operating condition.

This variance was mostly seen in level controllers because they are snap

- acting a nd operate in an on or off type condition. The level controller samples showed a range of values depending on how many dynamic events occurred over a sampling period. This was an expected outcome because level controllers have primarily static bleed rates with variable dynamic events. Our analysis captured both static and dynamic events over the sampling time frame and are both accounted for when using the average as a representative bleed rate for the samples with skewed and bimodal distribution.



### 6 .3 Manuf acturer Specification

Differences are observed between the average bleed rate and the manufacturer specification. It should be noted that this variability was expected. The manufacturer specification measures the steady

- state air consumption in a lab setti ng. The purpose of the survey is to determine the average bleed rate of pneumatic controllers and pumps when operating under
real field conditions. The field bleed rates differed from manufacturer rates because they are operating under real world
condition s with variability in dynamic and static action. For pumps, the field bleed rates are different than manufacturer rates because they are provided with a maximum air consumption using a maximum supply pressure.



### 6 .4 Gas vs. Air vs. Propane

The majority of th e field samples were taken at sweet w ell sites; however, as devices became harder to find, the sampling team targeted compressor stations and bat teries as well as sour sites in order to reach the 30 sample threshold. A handful of air samples were obtained from larger facilities because typicall y, bigger facilities run compressed air instead of

pressur iz ed natural gas. A handful of propane samples were obtained from sour well sites because an alternative to

process natural gas is required at sour sites, so p ressurized propane is typically used. Air and pr opane samples were
corrected using a density ratio in order to compare equivalent volumes of natural gas bled.21 | P a g e



### 6 .5 Calscan Vent Gas Meter

When determining which metering device to use during sampling, t he Ca lscan bellows meter was chosen over the Bacharach High

- Flow S ampler because it has greater accuracy and has the ability to capture the static and dynamic bleed

rates (see Section 2.3 .1 for a complete discussion on the differences) . A drawback exists; howev er, that should be discussed and may explain some of the variability in the data. It is well

- known that metering a device can affect the operation of the device when hooked up due to back pressure. It is possible that certain controllers didn’t produce eno ugh pressure when hooked up to overcome the back pressure, resulting in a zero reading.



### 6 .6 Producer and Sub

- District

A multitude of producers and sub districts within British Columbia were sampled; however, the purpose was not to
determine differences bet ween producers and sub

- districts but determine generic BC wide bleed rates that reflect values from a variety of locations and producers . Since we have taken our samples from a variety of fields, the average bleed

rate captures the variability between prod ucers and sub

- districts. The intent of the survey was not to determine whether producers and sub

- districts were influencing variables; however, the methodology was designed to ensure that th ese variables were accounted

.



### 6 .7 Adding a Device Model to the Su rvey

If a producer wishes to develop an average bleed rate for a controller or pump that was outside the scope of this survey, they can follow the sampling methodology outlined in Section 2. A minimum of 30 bleed rates per device model must be
achieved usi ng a mass flow meter from a variety of producing fields and producers. Please reference the Project Methodology (July 29 th 2013) report for the full protocol.



### 6.8 Mean vs. Median

Many of the snap acting level controllers had skewed or bi

- modal distributio n. Typically the median is used to represent the value for central tendency in non

- normal distributions; however, the goal for this project was to develop an average where all the samples are weighted equally. The mean is recommended because it weights all samples equally; whereas the median would neglect samples on the tail of the distribution. The median would not accurately reflect the combined static and dynamic bleed rate; whereas the mean places equal weight on each sample. Using the median as a measu re of central tendency would ignore the data that repr esents the dynamic action over the course of sampling. Thus, when calculating the most accurate bleed rate, the mean is more representative than the median because of static and dynamic
actions.

Figu re 11 : Non

- Normal Distribution22 | P a g e



### 6 .9 General Linear Model

A general linear model is a statistical test that analyzes variance between sample populations. It was chosen to compare the samples because some populations showed a non

- norm al distribution and because the samples sizes differed. This statistical test incorporates different ANOVA tests and non

- parametric tests to produce and accurate p

- value. A general linear model is more robust when dealing with different normalities and var iance in sample populations compared to a
standard ANOVA and was therefore selected (Bolker et al. 2009)

.



### 6.10 Summer vs. Winter Sampling

The sampling was performed over the summer months of August and early September. The Calscan Hawk 9000 meter
normalize d all the samples for temperature and pressure differences in order to eliminate the different operating
variable s

. A known constraint of our sampling methodology was that not all chemical injection pumps operate in the
summer months . To eliminate this iss ue, the sampling team would turn the pump on and perform samples at three normal operating speeds (high, medium and low strokes per minute).



# 7. Applications of the Analysis

The mean bleed rates calculated in this survey are applicable for GHG reporting. A de cision tree is provided in

Figure 12 and Figure 13 below to aid producers in determining which bleed rate to apply.

Figure 12 : Controller Decision Tree23 | P a g e

Figure 13 : Pump Decision Tree



# 8. O bservations

This study involved comprehensive analysis of pneumatic devices and a significant fieldwork program. This, combined with the fact that this study was one of the first of its kind, resulted in the research team observing several instances where the general body of scientific knowledge in this area could be advanced. These are outlined below:

1. Dynamic vs. static bleed rates : By nature, controller devices have a baseline bleed rate with dynamic events where

more ga s may be vented. In this study, the maximum sampling time was set at 30 minutes for each device, which added variability to the amount static and dynamic action captured in the sampling of snap

- acting controllers (see section 6.2 for further discussion). The time interval between dynamic events may be longer than a 30 minute cycle. Future surveys investigating intermittent bleeding controllers, if undertaken, may consider capturing two complete dynamic cycles if reasonably practicable

.

2. Categorisation of ‘h igh’, ‘intermittent’, and ‘low bleed’: The survey was focused around h igh

- bleed pneumatic
controllers. I t was observed that for some high bleed devices the calculated mean bleed rate fell below the 0.17m3/hr WCI high bleed threshold and some tested low ble ed controllers were higher. As an analysis of the observed differences were not within the scope of the project no further work to assess possible cause was undertaken, at this
time. It is observed that f uture studies may consider all types of pneumatic c ontrollers so that categorisation can be more fully tested and to ensure that field tested emission factors are available for all emitting pneumatic devices.24 | P a g e



# 9. Conclusion

The purpose of the survey was to determine a representative average bleed rate for hig h bleed pneumatic controllers
and pneumatic pumps when operating under field conditions. All outcomes were achieved. The results of the analysis led to the development of three generic bleed rates and twenty specific bleed rates for common pneumatic contro llers and pumps for BC’s oil and gas industry. These bleed rates can be used in the development of emission factors for GHG reporting and potentially offset purposes.


# References

Allen et al. (2013). “Measurements of methane emissions at natural gas produc tion sites in the United States”. PNAS.
Accessed: November 8 th 2013.

BC Oil and Gas Commission ( 2013 ) Unpublished report.

Bolker et al. (2009). “Generalized Linear Mixed Models”. Trends in Ecology and Evolution 24 (3) : 127

- 135. Accessed:
November 5 th 2013.

CAPP . (2008) . “ Efficient Use of Fuel Gas in Chemical Injection Pumps. Fuel Gas Best Management Practices ” . Accessed: July 9, 2013.

CAPP . (2008) . “ Efficient Use of Fuel Gas in Pneumatic Instruments ”

. Fuel Gas Best Management Practices

. Accessed: July 9, 2 013.

CAS (2013). Draft Schedule A from BC Climate Action Secretariat for Pneumatics Emission Factor Determination.

EPA 40 CFR 98: Mandatory Greenhouse Gas Reporting. Subpart W: Petroleum and Natural Gas Systems. Accessed:
November 2013. http://cfr.regstoday.com/40cfr98.aspx#40_CFR_98pSUBPART_W

EPA. (2006). “ Options for Reducing Methane Emissions from Pneumatic Devices in the Natural Gas Industry ”

. EPA Gas
STAR . Accessed July, 8 2013.

Fisher Controls. (2012). “ Fisher 2500

- 249 Pneumatic Controllers and Transmitters ” & “Fisher 4660 High

- Low Pressure Pilot” & “4156K and 4166K Series Wizard II Temperature Controllers”

. Product Bulletin . Accessed July 9, 2013.
McClave, J.T. and Sincich , T. (2003). “ Statistics, Ninth Edition, Annotated Instructor’s Edition ” . Prentice Hall, New Jersey: Upper Saddle River.

Pacific Carbon Trust. (2011). “High

- Bleed to Low

- Bleed Conversion for Pneumatic Controllers”. Meta

- Protocol for Oil and
Gas Emission Reductions Projects . Accessed: July 8, 2013.

Western Climate Initiative. (2011). Final Essential Requirements of Mandatory Reporting. Amended for Canadian
Harmonization

– Second Update . Accessed July 8, 2013.27 | P a g e



# Appendix A : Compilation of All Known Pneumati c Controllers

Description Manufacturer Model Manufacturer Rate (m 3 /h NG) Source Equivalent Devices Sample? Justification

Pressure
Controller Ametek Series 40 0.22 WCI/CAPP BMP/GAS Star Yes High

- bleed

Positioner Becker HPP

- 5 0.18 WCI Yes High

- bleed

Pre ssure Controller Bristol

Babcock Series 502

A/D 0.22 WCI/CAPP BMP/GAS Star Yes High

- bleed

Pres s ure Controller Dyna

- Flo 4000 0.89 WCI Dyna

- Flo 5000 Yes High

- bleed

Pres s ure Controller Dyna

- Flo 4000LB 0.13 Dyna

- Flo Yes High

- bleed

Transducer Fairchild TXI 7800 0.31 WCI Yes High

- bleed

Transducer Fisher 546 1.10 WCI Fisher 546S
Fisher 546 Yes High

- bleed

Transducer Fisher 646 0.29 WCI Yes High

- bleed

Transducer Fisher 846 0.44 WCI/CAPP BMP/GAS Star Fisher 846S Yes High

- bleed
Level
Controller Fisher 2500 1.55 WCI Yes High

- bleed

Level
Controller Fisher 2900 0.85 WCI/CAPP BMP/GAS Star Fisher 2901
Fisher 2900A Yes High

- bleed

Positioner Fisher 3582 0.59 WCI Yes High

- bleed

Positioner Fisher 3590 1.10 WCI Yes High

- bleed

Positioner Fisher 3660 0.26 WCI Yes High

- bleed

Positioner Fisher 3661 0.38 WCI Yes High

- bleed

Pressure
Controller Fisher 4100 1.83 WCI Fisher 4101 Yes High

- bleed

Pressure
Controller Fisher 4150 0.96 WCI Fisher 4150K Fisher
4160R CVS 4150 Yes High

- bl eed28 | P a g e

Description Manufacturer Model Manufacturer Rate (m 3 /h NG) Source Equivalent Devices Sample? Justification

Fisher 4150K Fisher
4160 Temperature
Controller Fisher 4156 Fisher 4156
Fisher 4166 Yes High

- bleed

Pressure
Controller Fisher 4194 0.16 WCI Yes High

- bleed

Pressure
Controller Fisher 4195 0.16 WCI Yes High

- bleed

High

- Low
Pressure Pilot Fisher 4660 0.18 Gas STAR Fisher 4660A Yes High

- bleed

Positioner Fisher Fieldvue
DVC5000 0.37 WCI/CAPP BMP/GAS Star FisherDVC5040
FisherDVC5030
FisherDVC5020
FisherDVC5010 Yes High

- bleed

Level
Controller Fisher 2900A Fisher 2901A Yes High

- bleed

Positioner Fisher 3582i 0.76 WCI Yes H igh

- bleed

Positioner Fisher 3620J 0.98 WCI Yes High

- bleed

Pressure
Transmitter Fisher C1 0.19 WCI Yes High

- bleed

Positioner Fisher Fieldvue
DVC6000 0.52 WCI/CAPP BMP/GAS Star FisherDVC6030
FisherDVC6020
FisherDVC6010 Yes High

- bleed

Transducer Fisher i2P

- 100 0.37 WCI Fisher i2P

- 100, 4

- 20mA Yes High

- bleed

Pressure
Controller Foxboro 43AP 0.66 WCI/CAPP BMP/GAS Star Yes High

- bleed

Level
Controller Invalco AE

- 155 1.95 WCI Yes High

- bleed29 | P a g e

Description Manufacturer Model Manufacturer Rate (m 3 /h NG) Source Equivalent Devices Sample? Justification

Level
Controller Invalco CT Series 1.47 WCI/CAPP BMP/GAS Star NAT CO Flextube (CT Series) Yes High

- bleed

Positioner Invalco Flextube (CT Series) 1.47 WCI NATCO Flextube (CT Series) Yes High

- bleed

Pressure
Controller ITT Barton 338 0.22 WCI/CAPP BMP/GAS Star Yes High

- bleed

Pressure
Controller ITT Barton 4195 0.13 Gas Star Yes High

- bleed

Pressure
Controller ITT Barton 335P 0.22 WCI/CAPP BMP/GAS Star Yes High

- bleed

Level
Controller Kimray Gen2 0.54 Manufacturer’s website 14 Yes High

- bleed

Temperature
Controller Kimray HT

- 12 Yes High

- bleed

Level
Controller Mallard 3201 Yes High

- bleed

Positioner Masoneilan 4600B Series 0.88 WCI Yes High

- bleed

Positioner Masoneilan 4700B Series 0.88 WCI Yes High

- bleed

Positioner Masoneilan 7400 Series 1.36 WCI Yes High

- bleed

Positioner Moore

Products 73N

- B PtoP 1.33 WCI/CAP P BMP/GAS Star Yes High

- bleed

Positioner Moore

Products 750P 1.55 WCI/CAPP BMP/GAS Star Yes High

- bleed

Transducer Moore

Products IPX2 Yes High

- bleed

14 http://mobile.kimray.com/downloads/instruction/GENIIBACKmount.pdf30 | P a g e

Description Manufacturer Model Manufacturer Rate (m 3 /h NG) Source Equivalent Devices Sample? Justification

Pressure
Controller Natco CT 1.55 WCI Yes High

- bleed

Pressure
Controller Norriseal 4900 Yes Hi gh

- bleed

Level
Controller Norriseal 1005PI Yes High

- bleed

Pressure
Controller Time Mate 2000 Yes High

- bleed

Level
Controller Wellmark 2001A 0.13 CAPP Yes High

- bleed

Positioner YTC YT

- 2400 Yes High

- bleed
Level
Controller Fisher 2660 0.04 CAPP BMP Fisher 2660A Yes PCT

Level
Controller Fisher 2680 0.04 CAPP BMP Fisher 2680A Yes PCT

Level
Controller Fisher L2 0.06 WCI Yes PCT

Level
Controller Norriseal 1001 0.07 WCI 1001A No PCT

Level
Controller Norriseal 1001XL 0.07 WCI No PCT

Positioner Becker ERP

- 2.0 0.00 WCI/CAPP BMP/GAS Star No Low

- bleed
Controller Becker VRP

- SB 0.00 Gas Star No Low

- bleed

Pressure
Controller Bristol

Babcock 358 0.07 Gas Star No Low

- bleed

Pressure
Controller Bristol

Babcock 359 0.07 Gas Star No Low

- bleed

Pressur e Controller Bristol

Babcock 5455 Model

624

- III 0.09 WCI No Low

- bleed31 | P a g e

Description Manufacturer Model Manufacturer Rate (m 3 /h NG) Source Equivalent Devices Sample? Justification

Pressure
Controller Bristol

Babcock Series 5453

-

Model 624

- II 0.11 Gas STAR No Low

- bleed

Pressure
Controller Bristol

Babcock Series 5455

Model

- 624
10F 0.11 WCI/CAPP BMP/GAS Star No Low

- bleed

Pressure
Transmitter Bristol

Babcock Series 5457

-

70F 0.11 Gas STAR No Low

- bleed

Transducer Bristol

Babcock Series 9110

-

00A 0.02 WCI/CAPP BMP/GAS Star No Low

- bleed

Level
Controller Fisher 2100 0.04 WCI/CAPP BMP/GAS Star No Low

- bleed

Positio ner Masoneilan SVI Digital 0.04 CAPP No Low

- bleed

Positioner VRC VP700G 0.04 WCI/CAPP BMP/GAS Star No Low

- bleed32 | P a g e



# Appendix B : Initial List of Pneumatic Devices Included in the Sample

Pneumatic Controller Lis t

This list was developed by analy zing the frequency each controller make/model appeared in Cap

- Op’s field sample database. These 15 controllers make up 97% of the database.

Description Manufacturer Model Equivalents Name Count Percentage

Pressure
Controller Fisher 4150 Fisher 4150K Fish er 4160R CVS 4150
Fisher 4150K Fisher
4160 Fisher 4150 380 26.44%

Transducer Fisher i2P

- 100 Fisher i2P

- 100, 4

- 20mA Fisher i2P

100 177 12.32%

Level
Controller Norriseal 1001 1001A Norriseal
1001 170 11.83%

Level
Controller Fisher 2900 Fisher 2901 Fisher
2900A Fisher 2901A Fisher 2900 163 11.34%

Transducer Fisher 546 Fisher 546S Fisher
546 Fisher 546 94 6.54%

Level
Controller Fisher L2 Fisher L2 84 5.85%

Level
Controller Fisher 2680 Fisher 2680A Fisher 2680 78 5.43%

High

- Low
Pressure Pilot Fisher 4660 Fisher 4660A Fisher 4660 73 5.08%

Positioner Fisher Fieldvue
DVC6000 FisherDVC6030

FisherDVC6020
FisherDVC6010 Fisher

Fieldvue
DVC6000 39 2.71%

Temperature
Controller Kimray HT

- 12 Kimray HT

- 12 27 1.88%

Level
Controller Fisher 2660 Fisher 2660A Fisher 2660 24 1.67%

Level
Controller Fisher 2500 Fisher 2506 Fisher 2500 23 1.60%

Level Switch SOR 1530 SOR 1530 23 1.60%

Pressure
Transmitter Fisher C1 Fisher C1 19 1.32%

Level
Controller Norriseal 1001XL Norriseal
1001XL 19 1.32%

Total 97%33 | P a g e



# Pneumatic Pump List

This is a comprehensive list of known pump models that exist in the field. The list was developed by surveying multiple sources (industry, m anufacturers, etc.). First round sampling uncovered which

pumps were common (see bolded below) in order t o target were sampling.

Manufacturer Model

Arrow 548

Arrow 5100

Bruin 5000

Bruin BR113LP Checkpoint 1250

COE 5100

CVS 5100

CVS C

- 252

Flowmore 5100

Graco 716 Ingersoll Rand

- Linc 84

- T Series

Linc 282
Morgan 4500
Morgan HD312

Plainsman

-
T exst eam 5100

Timberline 2500, 5000, 1560 Series Western Chemical Pump ACE Series
Wilden 5000

Williams P125

Williams P250

Williams P50034 | P a g e



# Appendix C: Field Sampling Guide

The subsequent guidelines were taken from the full Sampling Methodology report and were followed by the GreenPath field sample team:

What to Sample On

- Site

 Sample all pneumatic controllers (including their equivalents) from the list provided ( found in Appendix B )

.

 Sample all pumps. If pumps are turned off and you have permission from the operator to turn it on, take separate samples of the pump at different operating speeds (high, medium and low strokes per minute) . Limit different operating speeds to speeds that the pump would function under normal operati ng conditions

. Duration of Sampling

All samples need to:

 be taken for at least 30 min, or

 until 2 ft 3 of gas has been collected

Sampling Device

 Attach the Calscan Hawk 9000 positive displacement bellows meter to the pneumatic device according to manufactu rer specification.35 | P a g e



# Appendix D : Box Plot Distributions

A box plot distribution graph is a way to show the distribution of categorical samples. The black line represents the median value for a sample population. The grey box represents the first and thir d quartile or 50% of the data. The black lines show the range of observations. The median is represented by the horizontal line and the mean is the circle in the grey box.

Figure 14 : Box Plot Distribution for High

- Bleed Controlle rs

Figure 15 : Box Plot Distribution for High Bleed Intermittent Controllers36 | P a g e

Figure 16 : Box Plot Distribution for Diaphragm Pumps

Figure 17 : Box Plot Distribution for Piston P umps37 | P a g e



# Appendix E : Bleed Rate Equation Coefficients

Pneumatic Controllers

A regression analysis showed that there was a positive correlation between certain pneumatic controller bl eed rates and supply pressure.

Table 10 : Controller Bleed Rate Equation Coefficients

C ontroller Mean Bleed Rate (m 3 /hr) Coefficient 15 R 2 Correlation 16
High Bleed Pneumatic Controllers 0.2605 0.0012 0.41 Positive
High Bleed Intermittent Controllers 0.2476 0.0012 0.35 Positive

Pressure Controller

Fisher 41 50 0.4209 0.0019 0.46 Positive

Fisher C1 0.0649 0.0003 0.25 Positive

Fisher 4660 0.0151

- 0.05 Weak

Level Controller

Fisher 2500 0.3967 0.0011 0.73 Strong

Fisher 2680 0.2679 0.0014 0.39 Positive

Fisher 2900 0.1447

- 0.13 Weak

Fisher L2 0.2641 0.0012 0.33 Positive
Murphy L1200 0.2619 0.0012 0.38 Positive

Norriseal 1001 0.1868

- 0.23 Weak

SOR 1530 0.0531

- 0.21 Weak

Positioners

Fisher DVC 6000 0.2649 0.0011 0.75 Strong
Temperature Controller
Kimray HT

- 12 0.0351

- 0.06 Weak

Transducer

Fairchild TXI 7800 0.1543 0.0009 0.60 Positive

Fisher 546 0.3547 0.0017 0.77 Strong

Fisher i2P

- 100 0.2157 0.0009 0.65 Strong

15 Controllers showing a weak correlation to supply pressure do not have a representative bleed rate equation and should therefore use the mean b leed rate instead of the emission equation.

16 Strong correlation indicates R 2 >0.64 Positive correlation indicates 0.25<R 2 <0.64 Weak correlation indicates R 2 <0.2538 | P a g e

Pneumatic Pumps

A regression analysis showed that there was a positive correlation between pneumatic pump bleed rates, supply pressure, injection pressure and strokes per minute. The most accurate bleed rate would take into account these 3 operating variables when calculating the bleed rate for a pneumatic pump.

Table 11 : Pump Bleed Rate Equation Coefficients

P neum atic Pump Mean Bleed

Rate (m 3 /hr) Supply Pressure

Coefficient (g) 17 Injection Pressure

Coefficient (n) 1 7 Strokes Per Minute

Coefficient (p) 1 7 R 2 Correlation 18

Diaphragm 1.0542 0.00202 0.000059 0.0167 0.68 Strong

Piston 0.5917 0.00050 0.000027 0.0091 0.49 Po sitive
Morgan HD312 1.1292 0.00418 0.000034 0.0073 0.66 Strong

Texsteam 5100 0.9670 0.00030 0.000034 0.0207 0.74 Strong

Williams P125 0.4098 0.00019 0.000024 0.0076 0.53 Positive

Williams P250 0.8022 0.00096 0.000042 0.0079 0.53 Positive

Williams P500 0.6969 0.00224

- 0.000031 0.0046 0.74 Strong

17 The coefficients are to be used in the pump bleed rate equation: Bleed Rate = m * (Suppl y Pressure in kPa) + n * (Injection Pressure in kPa) + p * (Strokes per Min)

18 Strong correlation indicates R 2 >0.64 Positive correlation indicates 0.25<R 2 <0.64 Weak correlation indicates R 2 <0.25