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A Spatiotemporal Industrial Activity Model for Estimating the Intensity of Oil and Gas Operations in Colorado William B. Allshouse, John L. Adgate, Benjamin D. Blair, and Lisa M. McKenzie Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02084 • Publication Date (Web): 17 Jul 2017 Downloaded from http://pubs.acs.org on August 3, 2017

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Environmental Science & Technology

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A Spatiotemporal Industrial Activity Model for Estimating the Intensity of Oil and Gas

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Operations in Colorado

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William B. Allshousea, John L. Adgatea, Benjamin D. Blaira, Lisa M. McKenziea*

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a

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Aurora, Colorado, USA

Department of Environmental and Occupational Health, Colorado School of Public Health,

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*

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Health, Colorado School of Public Health, 13001 E 17th Pl, Campus Box B119, Aurora,

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Colorado 80045 USA. Telephone: 303.724.5557. E-mail: [email protected]

Corresponding Author: Lisa McKenzie, Department of Environmental and Occupational

1 ACS Paragon Plus Environment


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Abstract Oil and gas (O&G) production in the United States has increased in the last 15 years and

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operations, which are trending towards large multi-well pads, release hazardous air pollutants.

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Health studies have relied on proximity to O&G wells as an exposure metric, typically using an

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inverse distance weighting (IDW) approach. Since O&G emissions are dependent on multiple

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factors, a dynamic model is needed to describe the variability in air pollution emissions over

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space and time. We used information on Colorado O&G activities, production volumes, and air

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pollutant emission rates from two Colorado basins to create a spatiotemporal industrial activity

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model to develop an intensity-adjusted IDW well count metric. The Spearman correlation

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coefficient between this metric and measured pollutant concentrations was 0.74. We applied our

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model to households in Greeley, Colorado, which is in the middle of the densely developed

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Denver-Julesburg basin. Our intensity-adjusted IDW increased the unadjusted IDW dynamic

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range by a factor of 19 and distinguishes high intensity events, such as hydraulic

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fracturing/flowback, from lower intensity events, such as production at single well pads. As the

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frequency of multi-well pads increases, it will become increasingly important to characterize the

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range of intensities at O&G sites when conducting epidemiological studies.

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Introduction Widespread implementation of technological advancements for oil and gas (O&G)

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extraction, such as horizontal drilling and hydraulic fracturing, have increased the spatial extent

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of economically viable O&G reserves, leading to more development near where people live.1

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The state of Colorado, which currently ranks as the 7th largest oil and 6th largest gas producing

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state, has at least 378,000 people living within 1 mile of an active O&G well.2-4 The increasing

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intersection of O&G development and homes has led to concern about the possible health

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consequences of residing near O&G operations. One focus of attention is that a wide range of

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hazardous air pollutants (HAPs), including diesel exhaust, benzene, ethylbenzene, toluene, and

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xylenes (BTEX), particulate matter, and polycyclic aromatic hydrocarbons (PAHs), are emitted

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during each stage of O&G well development.5-7 Exposure to HAPs associated with O&G

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development may affect the health of people living near the sites.8 In addition, people living near

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O&G development experience odors, noise, and night-time light, which are associated with

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increased psychosocial stress, sleep disturbance, and cardiovascular health.9-16

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In the absence of spatially and temporally resolved air monitoring at multiple locations,

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comprehensive emission inventories, and detailed meteorological data necessary for robust

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modeling approaches; proximity models have been used to retrospectively approximate exposure

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to HAPs and other stressors emitted from or associated with O&G wells.17-21 The first proximity

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models assumed that exposure to HAP emissions and other stressors were a function of the

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number of O&G wells within a specific buffer around a home, weighted by distance.17, 18, 20

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These first studies used an inverse distance weighted (IDW) count to estimate the density of

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O&G wells within 10 miles of residential address as an individual-level exposure metric. These

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studies found statistically significant associations between IDW well counts and congenital heart 3 ACS Paragon Plus Environment


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defects, acute lymphocytic leukemia, and low birth weights.17, 18, 20 These preliminary studies did

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not account for the differences in emissions among the phases of well development, levels of

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activity among well pads, or the impact of individual well pad infrastructure, such as valves and

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tanks.

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More recently, the IDW approach was expanded to include all wells regardless of

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distance from residence and separate IDW metrics for phases of well development (preparation,

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drilling, hydraulic fracturing, and production). One study in Pennsylvania used a sum of the

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normalized, phase-specific activity metrics and found an association between proximity to

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natural gas activity and both pre-term birth and high-risk pregnancy.19 Due to high collinearity

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among phase-specific metrics, another Pennsylvania study modelled phase-specific metrics

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separately and found an association between each phase of natural gas activity and asthma

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exacerbation.21 These expanded IDW models are able to estimate an individual’s exposure

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relative to other individuals within each well development phase, but only include one phase at a

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time and assume equivalent intensity of activity for all phases.

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However, levels of activities and the magnitude of HAP emissions differ by phase of

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development and emission controls.8, 22, 23 Drilling and hydraulic fracturing involve numerous

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truck trips and often use diesel engines to power the process. Flowback after hydraulic fracturing

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has been associated with some of the largest HAP emissions during the well development life

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cycle.8, 22, 23 Shortly after the well is completed, it goes into production, which brings

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hydrocarbons from the target formation to the surface, where they are either stored in tanks or

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sent into a pipeline. A well pad with a large number of tanks rather than pipelines is likely to

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have higher air pollutant levels due to tank emissions.24 In addition, the United States


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Environmental Protection Agency (EPA) estimates that O&G wells with emission controls (e.g.,

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green completions) can reduce O&G emissions at numerous infrastructure points by up to 95%.25

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The IDW model approach would be improved by accounting for both temporal inter and

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intra-well variability in intensity among well development phases, as well as the number of O&G

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wells and types of equipment on the pad, and the use of emissions controls. Due to the variety of

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HAPs that are produced at a well pad, as well as other stressors, selecting either one or multiple

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pollutants to model may not adequately approximate exposure to all stressors associated with

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O&G development. However, estimating the relative intensity at each well pad according to the

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type of activity (i.e., phase of well development), number of wells, and volume of production

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provides an estimate of cumulative exposure to multiple stressors and adds a temporal

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component to O&G exposure assessment that has not been fully developed to date. This

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temporal component adds intra-well variability across phases and within the production phase,

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making for a more dynamic cumulative exposure model.

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In this manuscript, we outline development and implementation of an industrial activity

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model for retrospectively estimating the temporal and spatial intensity of O&G development

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using data on activities and production volumes from the Colorado Oil and Gas Information

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System (COGIS). This approach builds upon previously used IDW models to better delineate

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exposure categories for health studies, especially during the short-lived, critical exposure

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windows that are theorized to exist for many developmental and childhood health effects.26-28

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Materials and Methods

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Data Sources 5 ACS Paragon Plus Environment


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We built a relational database of production volumes, well pad activities, and associated

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metadata from information in the COGIS to build a spatiotemporal model of O&G activity. The

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Colorado Oil and Gas Conservation Commission (COGCC) maintains the COGIS, which

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contains a wide variety of data about upstream O&G extraction in the state.29 From the COGIS,

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we obtained the location, dates of activities, and the amount of O&G produced by month for

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each well in Colorado. Dates of activities include the spud date (when drilling begins), treatment

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dates (when hydraulic fracturing occurs), whether green completion techniques were used to

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control flowback emissions, first production date, shut in dates (temporarily plugging a well),

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and abandonment date (permanently plugging a well). Based on a unique well pad identifier, we

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then determined which wells were co-located on multi-well pads. For pads with more than 2

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wells, we obtained the numbers of tanks on each well pad from the 2012 inspection report

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available in the COGIS database and verified this information using the Google Earth satellite

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image of the site that was closest in time to the inspection report. If a 2012 inspection report was

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not available, we used the closest report to the year 2012 and the Google Earth image that was

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the closest match to the report date. For pads with 1 or 2 wells, we assumed that equipment

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would not differ significantly at the site based on observations of these sites using Google Earth

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and data contained in inspection reports within the COGIS.

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Emissions data from two studies, one conducted in the Piceance Basin in Garfield

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County, Colorado between 2013 and 2015 and the other in the Denver-Julesburg Basin (DJB)

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along Colorado’s Front Range area between 2014 and 2016, were used to calibrate the relative

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phase specific intensity factors in our model.22, 23 These studies conducted experiments around

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several well pads undergoing a particular activity to determine emission rates for specific air

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pollutants during that activity. Activities included 5 well pads being drilled, 11 that were 6 ACS Paragon Plus Environment

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undergoing hydraulic fracturing/flowback, and 11 in production. Specific pollutant emission

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rates were determined by releasing a tracer of acetylene at the well pad, collecting a downwind

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ambient air sample, identifying the tracer in the downwind sample, and using the ratio of a

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specific background-corrected pollutant concentration, also measured in the downwind sample,

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to the tracer at the downwind location to derive the emission rate of the pollutant at the source.

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We obtained the addresses of all residential properties in Weld County, Colorado from

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the DataQuick Information Systems database30 (as of 2012). The Google Maps Geocoding

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Application Programming Interface (API) was used to geocode the addresses of these properties

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and we kept those whose geocode could be returned with “Rooftop” accuracy and was within 2

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miles of the Greeley, Colorado city limit (N=32,737). Greeley sits in the middle of the DJB and

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has notable O&G operations in and around city limits.2 We modelled the intensity of O&G

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activities within 10 miles of this population (Eq. 1) and compared the modelled O&G activity to

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the IDW exposure metric that does not adjust for intensity.

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Model Development and Parameters

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We used the information from COGIS to inform the construction of our spatiotemporal

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model (Eq. 1) that estimates monthly intensity of O&G activity at each well pad, based on the

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specific events (pad preparation, drilling, hydraulic fracturing and flowback, and production),

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production volume, and O&G infrastructure. The time-varying intensity from a well pad can be

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estimated as:

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∑ = ∑ , + , + , + + +

(1)



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, where ZL(tm) is the intensity score Z for well pad L during month tm. This intensity score is a

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summation of activities at each of the nw number of wells on the pad over the nd number of days

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in the month. The variable IP,j indicates whether the pad is being prepared on day j, and we

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assume that this occurs 30 days prior to the first well spud date on the pad.19, 21 The variable ID,ij

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indicates whether well i is being drilled on day j, with the assumption that drilling begins on the

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spud date and whose duration is dependent on the year and the total well depth. Based on

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available information, we assume that it took 48 hours to drill 1000 feet in 2008 and 20 hours to

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drill 1000 feet in 2013.31, 32 We use these two time points to estimate the time needed to drill a

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well in a particular year, based on its measured depth. For wells that we did not have a measured

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depth, we used the average number of days needed to drill a well for that year based on the wells

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that did have a measured depth. The variable IF,ij indicates whether well i is undergoing hydraulic

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fracturing/flowback on day j. Hydraulic fracturing and flowback typically last 2-4 days and 7-12

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days, respectively.22 Based on the data available in the COGIS, we cannot distinguish between

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these two activities and assume that both fracturing and flowback begins on the (hydraulic

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fracturing) treatment date from COGIS, and that the process lasts 14 days. The parameter αi

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represents whether green completion was used during the hydraulic fracturing/flowback phase.

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This parameter is a function of control device efficiency, rule effectiveness, and capture

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efficiency. Using the Colorado Department of Public Health and Environment’s parameters for

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the 2008 state implementation plan of 95% control device efficiency, 83% for rule effectiveness

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and 75% capture efficiency for tank emissions, we calculate that αi=0.41 for green completions

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and αi=1 for uncontrolled completions.33 The variables Oi and Gi represent the percentile of

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O&G produced by well i, respectively, compared to oil (or gas) production of all active wells by


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month. The variable TL is the number of tanks on the well pad as determined by using inspection

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reports and Google Earth.

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Model Intensity Factors

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The intensity vector β was based on the sum of BTEX, propane, ethane, and n-hexane

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emission rates from the previously described O&G phase-specific emissions experiments.22, 23

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We derived our intensity factors using respective medians of the log-transformed sample

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emission rates for these eight O&G pollutants for each activity phase (drilling, hydraulic

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fracturing/flowback, and production). Monthly oil (β3) and gas (β4) production intensity were

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each set to 5, and other phase intensities were calculated relative to the production intensity using

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the emission rate data. Given that a well with an average O&G production would have a

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production intensity of 5, we then used the ratio of the median log-transformed drilling emission

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rate to that of the production emission rate to obtain our drilling intensity factor and used the

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same method for deriving our hydraulic fracturing/flowback intensity. These calculations yielded

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a relative drilling intensity of β1=25 and hydraulic fracturing/flowback relative intensity of 60 for

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controlled flowback. Since all flowback emissions experiments were conducted at sites using

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green completion techniques, we used the green completion emission reduction factor (αi), to

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obtain a relative intensity β2 =146 for uncontrolled flowback. See Supporting Information for

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more detail on deriving intensity factors.

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The tank intensity (β5) was estimated using data on total uncontrolled volatile organic

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compounds (VOC) emissions and the corresponding number of tanks subject to an EPA consent

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decree against Noble Energy.33, 34 The total uncontrolled VOC emissions were divided by the

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number of tanks in the battery and multiplied by αi to obtain the VOC emissions per tank. Since



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individual VOCs were not available for tanks, the median of the log-transformed total VOC

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emissions per tank were compared to the log-transformed total VOC emissions from the phase-

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specific emissions experiments, yielding β5 = 0.2.

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Model Validation

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To validate our model, we utilized results from two studies that collected ambient air

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samples in areas of dense O&G development and analyzed the samples for non-methane

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hydrocarbons. The first was a Garfield County study that collected 24-hour average ambient air

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samples within 350 feet in each cardinal direction of four well pads each undergoing drilling and

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completions in 2008.35 This study also collected samples at eight “background” sites, which

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given the high density of O&G development in that area can be considered representative of the

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production sites in their vicinity. We supplemented the Garfield County data with results from

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ambient air grab samples collected at nine locations across the DJB in 2014.36 We included

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locations where samples were collected on multiple days and were not next to gas plants,

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injection wells, or within 150 feet of O&G well equipment. The results for the sum of the eight

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O&G pollutants collected at each site were averaged over the number of samples collected. We

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used the model described in Eq. 1 to calculate an intensity-adjusted IDW (IA-IDW) at the 25

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locations where ambient air samples were collected. The IA-IDW well count for each sampling

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location was determined by identifying all O&G well pads within 10 miles of the sample;

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dividing the corresponding intensity factors (ZL) by the square of the distance between the

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sample and pad; and summing over the pads. The Spearman correlation was used to compare the

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log-transformed IA-IDW to the log-transformed average sum of measured O&G pollutant

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concentrations.


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Model Implementation

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The contribution of each model parameter was evaluated by setting the corresponding β

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parameter to 0 to evaluate the effect of the variable’s absence on the distribution of the monthly

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well pad intensity scores for 2004-2011. We modelled the intensity of O&G activities for all

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O&G wells within 10 miles of residences in Greeley and calculated an IA-IDW for each

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residence. We then compared the IA-IDW to an unadjusted IDW exposure metric. The

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unadjusted IDW calculation is the same as the IA-IDW, but uses an intensity factor of 1 for each

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active O&G well (i.e. a well pad with 10 wells would have ZL =10).The relative change in

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variance was used for comparison as our goal is to account for differences in intensities.

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Results Table 1 summarizes each parameter’s contribution to the intensity model. The hydraulic

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fracturing/flowback term had the largest effect on the variability of model outputs due to the

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magnitude of its intensity and its relatively short 14 day duration. The production stage variables

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had the biggest impact on the central tendency of the distribution due to both the intensity and

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long duration, which lasts years as opposed to months for the other stages. Drilling had a smaller

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impact on the model due to its short duration, less frequent re-occurrence, and lower intensity of

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activities compared to hydraulic fracturing/flowback.

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Model

Mean

Median

Variance (σ2) of

Range of 11

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Intensity (Unit-less)


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Intensity (Unitless)


Full Model

6.33

4.20

225

0.003-1440

Pad Preparation (β0=0)

6.34

4.20

225

0.003-1440

Drilling (β1=0)

6.19

4.18

213

0.003-1440

Hydraulic Fracturing/Flowback (β2=0)

5.25

4.18

49.7

0.003-259

Oil Production (β3=0)

4.40

2.36

201

0.003-1440

Gas Production (β4=0)

4.90

2.84

253

0.032-1413

Tanks (β5=0)

6.28

4.19

223

0.003-1440

233 234 Table 1. Each parameter’s contribution to the industrial activity model outputs by setting that factor (β) to 0. 235

The distribution of intensity scores for active well pads is shown in Figure 1. This

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distribution has two main components: 1) smaller producing well pads whose intensity scores

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range from 0.003 to 10 (-5.8 to 2.3 when log-transformed); and 2) pads with wells being drilled,

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hydraulically fractured/flowback, or large multi-well production pads whose intensity scores

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range from 10 to 1440 (2.3 to 7.3 when log-transformed). The full model produced an overall

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mean intensity of 6.33 with a variance of 225, and the mean monthly intensity generally

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increased over time, corresponding to the increase in well starts and related activity.


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Figure 1. The distribution of the log-transformed monthly modeled well pad intensity for O&G well pads in

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Colorado from January 2004-December 2011. Intensity scores below 2.3 (10 on a linear scale) generally reflect

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smaller pads in the production phase whereas scores above 2.3 represent large multi-well production pads or those

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pads undergoing drilling or hydraulic fracturing/flowback.

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Figure 2 shows an example of how the relative intensity at a well pad can change over

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time using our model. The intensity profile reflects the transformation of a single well pad into a

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large 21-well production pad. The initial intensity is based on the relatively small volume of

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O&G produced at the single well site. There are 20 additional wells drilled over the period of 5

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months and then these wells are all hydraulically fractured in the same month, leading to a spike

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in intensity. Following the hydraulic fracturing/flowback period, the 21 wells go into production

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and reflect an intensity of the increased production volume and additional equipment on site.

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Figure 2. An example of the change in temporal intensity at a well pad in Garfield County. This well pad was a

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single well production pad and transformed into a large 21-well production pad. Over the period of 5 months, these

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20 additional wells were drilled and then they were hydraulically fractured in the same month, where the intensity

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spiked.

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As depicted in Figure 3, the Spearman correlation coefficient between the log-

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transformed IA-IDW and log-transformed measured average sum of pollutant concentrations was

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0.74 (p

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