Storm Water Runoff from Natural Gas Well Sites in North Texas: Sampling, Modeling, and Management Options

Transcription

1 Storm Water Runoff from Natural Gas Well Sites in North Texas: Sampling, Modeling, and Management Options Kenneth E. Banks David J. Wachal City of Denton, Texas David Wachal is now with ESRI Professional Services Research demonstrated within this presentation was partially financed through grants from the U.S. Environmental Protection Agency US Natural Gas Development end of 2010 Texas through 2011: 100,966 I yr. increase of 5952, ~6.3% Source: The U.S. Energy Information Administration (EIA) Number of Producing Gas Wells Release date : 4/30/2012 1

2 Study Area Denton, Texas Population ~117,000 Denton City Limits ~160 sq km Denton ETJ ~207 sq km Rainfall averages 99 cm / yr In top 10 list of fastest growing cities in the nation 2

3 Cumulative disturbance area for wells drilled Study Area 3

4 Sediment 4

5 Heavy Metals Petroleum Hydrocarbons 5

6 Gas well concerns intensify in urban settings Concerns include air emissions, surface contamination, groundwater, noise and light pollution, safety, road damage, property values, effects on soil stability, etc.. NIMBY now includes NUMBY in the urban setting. Research Questions Is storm water runoff from natural gas well development sites impacted? If so, what impacts exist and to what extent? Can impacts be modeled? Can impacts be mitigated? Do impacts indicate runoff from these sites should be regulated? 6

7 Research Objectives 1. Characterize storm water runoff Sediment Petroleum Hydrocarbons Heavy Metals Water Chemistry 2. Evaluate modeling approaches 3. Develop model parameters using rainfall simulation 4. Evaluate Best Management Practices (BMPs) (modeling-economics) Study Design and Data Collection 7

8 Natural Gas Well Site Monitoring Point Pad Site Cut Slope Runoff Intake Pressure Transducer 8

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11 Discretion is the better part of valor, Act 1 Valor > discretion Discretion is the better part of valor, Act 2 Discretion > Valor 11

12 Sampled Events n=17 n= Site 2 Rainfall (mm) n=11 n=5 n=5 Site 3 Site 4 Site 2R 40 Site 3R mm = 1 inch Parameters Analyzed Water Chemistry Metals Petroleum Hydrocarbons Sediment Alkalinity Arsenic TPH TSS Calcium Cadmium BTEX Turbidity Chlorides Chromium Conductivity Copper Hardness Iron ph Lead TDS Manganese Nickel Zinc 12

13 Wilcoxon Rank-Sum and Ratio of Medians (Provided for those with an adequate number of samples above detection) Parameter n/n p-value Ratio Alkalinity 40/10 < Chlorides 40/ Conductivity 40/10 < Hardness 40/10 < ph 40/10 < TDS 40/ Calcium 36/8 < TSS 39/8 < Turbidity 37/9 < Iron 36/8 < Manganese 36/8 < Nickel 36/ Metals Previous Research Hudak and Banks (2006) Urban Watersheds Gas Well Site Metals Reference Site Metals Kayhanian et al. (2001) Highway Construction Sites Gas Well Site Metals Reference Site Metals = 13

14 Wilcoxon Rank-Sum and Ratio of Medians Parameter n/n p-value Ratio Alkalinity 40/10 < Chlorides 40/ Conductivity 40/10 < Hardness 40/10 < ph 40/10 < TDS 40/ Calcium 36/8 < TSS 39/8 < Turbidity 37/9 < Iron 36/8 < Manganese 36/8 < Nickel 36/ Sediments and Petroleum Hydrocarbons Sediment yields at gas well sites in this study ranged from 29 to 41 t/ha/yr TPH was below detection limits for all samples BTEX was detected in a few gas well sample but EMCs were below the detection limit BTEX was detected in all samples at reference site 2R TPH / BTEX did not exceed State WQ Standards 14

15 How much is 41 tonnes?? = 2.24 loads Sediment Concentration (mg/l) Wolman and Schick (1967) Daniel et al. (1979) Madison et al. (1979) Schueler and Lugbill (1990) Nelson (1996) USGS (2000) Kayhanian et al. (2001) EMC Range COD - Hickory Creek Reference Sites Gas Well Sites , ,000 15

16 City Of Denton Quarterly Sampling Summary Petroleum Hydrocarbons were not prevalent in storm water runoff at gas well sites Metals at gas well sites were: higher than at reference sites higher than local urban storm water similar to highway construction sites Total Suspended Sediment at gas well sites were: 157 times higher than at reference sites 36 times higher than at the outlet of the Hickory Creek Watershed Similar to other types of construction sites 16

17 Evaluation of Runoff and Sediment Yield Predictions Evaluate WEPP predictions of runoff and sediment Why monitoring data is limited can be used to develop what if scenarios provide defendable information for local decision making Allowed modeling of cut and fill slopes as a system Steps in Model Calibration Set parameters (management conditions; soil conditions, etc.) Input slope information Input climate information Run the model Compare observed and predicted values Error = Observed minus Predicted Evaluate error results using goodness-of-fit indicators Adjust parameters and rerun the model 17

18 Performance Ratings (Moriasi et al., 2007) NSE = Nash Sutcliffe efficiency (NSE) RSR = ratio of RMSE(root mean square errors) to standard deviation Percent bias (PBIAS) = describes the average tendency of simulated data to be larger or smaller than their observed counterparts. NSE ranges between negative infinity and 1.0, with NSE = 1 being the optimal value. RSR varies from an optimal value of 0 (which indicates zero RMSE) to a large positive value. The better the model simulation performs, the lower the RMSE, and therefore the lower the RSR score. Model Validation (17 events) NSE = Very Good RSR = Very Good PBIAS = Good/Very Good NSE =.90 RSR =.28 PBIAS =15 NSE =.86 RSR =.38 PBIAS =-11 NSE =.99 RSR =.12 PBIAS =-2 NSE =.86 RSR =.38 PBIAS =16 18

19 Summary The results demonstrate that WEPP can effectively model runoff and sediment from natural gas well sites Therefore, the model can be used to Evaluate potential sediment impacts Evaluate management alternatives to minimize impacts Work suggested future rainfall simulation study to better parameterize the pad portion of the site. From a storm water perspective, the pad is quite complex. Rainfall Simulation Study Design Dry Run - 58 mm/hr on existing soil conditions Wet Run - 58 mm/hr, applied 24 hours after dry run Very Wet Run - 58 mm/hr increased to 104 mm/hr decreased to 58 mm/hr Applied 30 minutes after wet run 19

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21 Data Collection Modeling Used WEPP to model runoff and sediment from plot area for each plot and each run type (18 runs) Input calculated K i values (interrill erodibility (mass-time/length 4 )) Adjusted K ef (effective conductivity) until predicted runoff equaled observed runoff NSE =.90 = very good RSR =.30 = very good PBIAS = 13.7 = very good 21

22 Modeling Continued WEPP was run in continuous simulation mode (30-years) Predicted annual average sediment yields for pad sites (t/ha/yr) Ranged from 5.0 to 11.0 Average of 7.4 Results Interrill Erodibility (K i ) parameter for gas well pad sites = mean of 681,699 kg s/m 4 Hydraulic Conductivity (K ef ) parameter = mean of 30 mm/hr Sediment yield from the pad portion of a gas well site ranges from 5.0 to 11.0 t/ha/yr 22

23 Modeling Erosion and Sediment Control BMPs Evaluate the effectiveness of six BMPs specifically for natural gas well sites Demonstrate a practical approach for quantitatively evaluating BMP alternatives based on Soil type Slope condition Site management goals Implementation cost Methodology For each slope and soil combination sediment yields were modeled with and without BMPs BMP Efficiencies were calculated accordingly: ER = (SY without BMP SY withbmp )/ SY without BMP ER = (50 tons 10 tons) / 50 tons = 80% efficiency Mulching Filter Strip Silt Fence 23

24 Percent Slope Modeled Site Slopes % Slope 2.9% Slope 1.8% Slope Each slope was simplified to 9 slope segment for modeling

25 Methodology For each slope and soil combination sediment yields were modeled with and without BMPs BMP Efficiencies were calculated accordingly: ER = (SY without BMP SY withbmp )/ SY without BMP ER = (50 tons 10 tons) / 50 tons = 80% efficiency Results Sediment Yield without BMPs (tonnes/ha/yr) 1.8% slope 2.9% slope 4.5% slope K-factor 0.17 Sandy Loam K-factor 0.32 Clay Loam K-factor 0.43 Silty Clay Loam

26 BMP Cost / Efficiency Example (Moderate Slope/Moderate Erodibility) BMP Site Cost Site Management Goal $9,000 $8,000 $7,000 $6,000 $5,000 $4,000 $3,000 $2,000 $1,000 $0 Seeding Mulching Erosion Blanket BMP Site Cost Effeciency Efficiency Silt Fence Filter Strip Sediment Basin Site Management Goal measure of the acceptable level of reduced sediment yield through erosion prevention and sediment removal ER Findings With BMPs, sediment yields can be reduced by 50 to over 90 percent according to model results Soils and slope both influence BMP efficiency Methodology can be used to assist in the selection of BMPs according to various site factors and BMP site costs. Note: The Reasonable and Prudent Practices for Stabilization (RAPPS) of Oil and Gas Construction Sites document used portions of this research to establish decision trees of management practices based on slope, rainfallrunoff erosivity factor (R), and soil characteristics 26

27 Conclusions Sediment yields at gas well sites in this study ranged from 29 to 41 t/ha/yr Metal concentrations were higher at gas well sites compared to reference sites and local urban watersheds WEPP was able to effectively model runoff and sediment yield from gas well sites Parameters were specifically developed for gas well pad sites modeled average sediment yields from the pad site were 7.4 t/ha/yr over 30 year period Best Management Practices could reduce sediment yields by 50 to 90% for entire site Recommendations Municipal / state governments should consider implementing erosion and sediment control provisions for natural gas development Types of erosion and sediment controls should be selected according to site-specific conditions. Selections can be refined by considering costs and efficiencies of BMPs Future research should focus on additional rainfall simulation studies and gas well site BMPs slope K factor Management options 27

28 Additional information Wachal, D.J. and K.E. Banks and D.H. Hunter Collecting Stormwater at Small Gas Well Exploration and Production Sites. Published in the Watershed and Wet Weather Technical Bulletin 11(6):10-15 Banks, K.E and D.J. Wachal "Water Quality and Land Use: Analyzing relationships among multiple watersheds using contingency analysis". Stormwater: January / February Wachal, D.J. and K.E. Banks Application of WEPP to Natural Gas Exploration and Production Sites. Published in Proceedings of the American Society of Agricultural and Biological Engineers (ASABE): Fourth Conference on Watershed Management to Meet Water Quality and TMDL Issues: Solutions and Impediments to Watershed Management and TMDLS. March Wachal, D.J. and K.E. Banks Sediment Impacts from Natural Gas Exploration and Production Sites. Published in the proceedings of 2007 Annual International Meeting of the American Society of Agricultural and Biological Engineers (ASABE). June 17-20, 2007, Minneapolis, Minnesota. Wachal, D.J. and K.E. Banks Integrating GIS and Erosion Modeling: A Tool for Watershed Management. Published in the proceedings of the Twenty-Seventh Annual ESRI International User Conference June 18-22, 2007, San Diego, California. Williams, H.F.L., D.L.Havens, K.E. Banks, and D.J. Wachal Field-Based Monitoring of Sediment Runoff from Natural Gas Well Sites in Denton County, Texas, USA. Environmental Geology DOI /s Wachal, D.J., R.D. Harmel, K.E. Banks and P.F. Hudak Evaluation of WEPP for Runoff and Sediment Yield Prediction on Natural Gas Well Sites. Transactions of ASABE 51(6): Wachal, D.J., K.E. Banks, P.F. Hudak and R.D. Harmel. Modeling Erosion and Sediment Control Practices with RUSLE 2.0: A Management Approach for Natural Gas Well Sites in Denton County, Texas, USA. Environmental Geology. DOI /s Havens, D.L Assessment of Sediment Runoff from Natural Gas Well Development Sites. Masters Thesis, University of North Texas, Denton, Texas. More information about WEPP can be found at (USDA Agricultural Research Service) Contact Information Kenneth Banks, Ph.D. City of Denton Phone: (940) [email protected] 28