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Using Produced Water and Drilling Trends to Explore Marcellus Shale Development in Pennsylvania

Using Produced Water and Drilling Trends to Explore Marcellus Shale Development in Pennsylvania. Seth Pelepko Bureau of Oil and Gas Planning & Program Management Shale Network Workshop May 12-13, 2014 State College, PA. Presentation Outline. Introduction Project Background

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Using Produced Water and Drilling Trends to Explore Marcellus Shale Development in Pennsylvania

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  1. Using Produced Water and Drilling Trends to Explore Marcellus Shale Development in Pennsylvania Seth Pelepko Bureau of Oil and Gas Planning & Program Management Shale Network Workshop May 12-13, 2014 State College, PA

  2. Presentation Outline • Introduction • Project Background • Regression Model Development • Dependent Variable • Independent Variables • Process • Model Results • Predictive Modeling • Model Inputs • Model Assumptions • Adjustments/Calibration • Simulation Results • The Future…

  3. Introduction Project Background • Water demands in association with unconventional shale development are significant • Flowback and produced water is highly mineralized and in certain cases cannot be effectively processed at wastewater treatment plants • As the Marcellus and other shale plays are developed in Pennsylvania, it is important for DEP to develop long-term, feasible strategies for managing produced water

  4. Introduction Project Background • Effective waste-stream treatment strategies, to a certain extent, are dependent upon the agency’s ability to forecast produced-water volumes based on drilling trends and well performance • Regression analysis represents one tool for conducting such predictive modeling

  5. Regression Model Development Dependent Variable • Produced water flow in bbls/day (Y) Independent Variables • Produced Gas (Mcf) (X1) • Estimated Ultimate Recovery (Bcf) (X2) • Production Time (days) (X3) • Latitude (dd) (X4) • Longitude (dd) (X5) • Gas Flow (Mcf/day) (X6)

  6. Regression Model Development Process • Dataset includes 35 wells throughout the play that have been in production since at least 2010 • Simple linear regression analyses were completed first • Variables were transformed using standard approaches to improve “goodness-of-fit” • Stepwise multiple linear regression analysis was conducted in SPSS using independent variables specific to each waste reporting period (2010-2, 2011-1, 2011-2, 2012-1, 2012-2, and 2013-1; n = 210)

  7. Regression Model Development

  8. Regression Model Development Process • Using the regression model dataset, it was generally observed that the highest volumes of produced water are noted during the first year of production – this is attributed to the influence of flowback

  9. Regression Model Development Model Results • Most important variables for predicting produced water flow (Y) are Production Time (X3), Latitude (X4), and Longitude (X5) • Regression equation is: ln(Y + 1) = -529.868 – 1.249ln(X3) + 72.646ln(-X5) + 60.094(X4) • Model is statistically significant (p<0.000) • Goodness-of-fit (R2) = 0.535 – approximately 54% of variance explained

  10. Regression Model Development Model Results • Comparing “observed” to “expected” outcomes Under-predicting waste flow Equivalency Line Over-predicting waste flow

  11. Predictive Modeling Model Inputs • Number and location of remaining Marcellus shale wells that can be drilled in Pennsylvania • Number and location of Marcellus shale wells currently in production (active) or drilled but not on-line (inactive) • Operational life of Marcellus shale well • Modeled waste quantity

  12. Predictive Modeling Model Assumptions • Urban areas and drilling units with >33% of area in the floodplain will not be developed • Areas where the Marcellus shale is less than 3,000 feet deep, southeast of Allegheny structural front, and the DRB will not be developed • Standard size of drilling units is 1 square mile (3,000 ft x 9,000 ft) and units are oriented NW to SE along major NW-SE fracture trend • 8 wells will be drilled on each unit

  13. Predictive Modeling

  14. Predictive Modeling Model Assumptions • Coordinates of future wells on drilling units that are not fully developed are derived by averaging coordinates of existing wells • Coordinates of future wells on drilling units that have not seen any development are derived by calculating the mean center of each drilling unit • Marcellus shale development can be approximated by randomly choosing future well locations from all available locations in the play • Approximately 3,000 wells will be drilled per year moving forward – none will be placed on inactive status and all are assumed to operate 365 days per year

  15. Predictive Modeling Model Assumptions • Of the inactive wells, all will be active within the first 5 years of the 30-year simulation and 20% will come on-line each year • Wells will be abandoned after equaling or exceeding 20 years of operation • No wells will be re-stimulated

  16. Predictive Modeling Modeling Approach • Logarithmic functions were used to estimate produced water volumes as a function of time since the produced water flow varies continuously

  17. Predictive Modeling Adjustments/Calibration • Lower Confidence and Prediction Limits were defined as a function of the expected outcomes and also modeled to calibrate with actual data

  18. Predictive Modeling Adjustments/Calibration • The model output for a group of 2,927 wells was compared to waste data for the reporting period 2013-1 • Waste data for 2013-1 were pro-rated based on the number of wells reporting produced fluid/flowback (4,368) compared to the number of wells in the simulation (2,927) • Model output was an average of the first 3 years of the simulation, assuming that most of the wells reporting production for 2013-1 were drilled in 2010-2, 2011, and 2012

  19. Predictive Modeling Adjustments/Calibration

  20. Predictive Modeling Simulation Results • The early peak in the data is related to a conservative model that assumes 3,000 wells drilled per year and that all the wells currently on inactive status come on-line over a 5-year period

  21. Predictive Modeling The Future… • In this scenario, annual produced water volumes will stabilize in 2034 somewhere between 2.4 billion gallons and 4.0 billion gallons • This interpretation is based on historical produced water trends (2010-2012) and the 50% PL and 75% PL models 0.04 0.07 0.31 0.51 0.41 0.68 1.65 2.74

  22. Seth Pelepko, P.G., Section Chief Well Plugging and Subsurface Activities Division 717.772.2199 mipelepko@pa.gov Thank You – Questions?

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