TY - JOUR
T1 - A data analytic workflow to forecast produced water from Marcellus shale
AU - Ettehadtavakkol, Amin
AU - Jamali, Ali
N1 - Funding Information:
We acknowledge the Bob L. Herd Department of Petroleum Engineering and the Whitacre College of Engineering at Texas Tech University for providing financial support for this research. We also appreciate the public datasets provided by the Pennsylvania Department of Environmental Protection and DrillingInfo. Use of academic licenses for Matlab™ and ArcGIS™ and general license for Python are acknowledged.
Funding Information:
We acknowledge the Bob L. Herd Department of Petroleum Engineering and the Whitacre College of Engineering at Texas Tech University for providing financial support for this research. We also appreciate the public datasets provided by the Pennsylvania Department of Environmental Protection and DrillingInfo. Use of academic licenses for Matlab™ and ArcGIS™ and general license for Python are acknowledged.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/1
Y1 - 2019/1
N2 - Water and gas production and potential water treatment facility requirements for the Marcellus formation are discussed using data analytic methods. These methods aim to handle dataset diversity and scale, and apply data analytics for statistical imputation, estimating future drilling activity, fluids production, and the optimization of water recycling facility locations and size. The objective of this study is to quantify and predict the volumes of produced fluids in the short- and medium-term for the Marcellus shale. The paper accomplishes this objective for the Pennsylvania section comprising 10,000 wells. The application of data analytics to large-scale, data-intensive, low-integrity public environmental databases is illustrated, and challenges of implementation methods are discussed and resolved. In addition, a special class of data analytic tools and workflows for spatiotemporal analysis (spatially correlated variation of parameters with time) is discussed and implemented. The results quantify the prospect of future drilling activity, and water and gas production for all Pennsylvania counties in the Marcellus. Finally, several practical problems of interest on applications of predictive analytics and management support are proposed and solved. The limitations of the proposed workflow are briefly discussed.
AB - Water and gas production and potential water treatment facility requirements for the Marcellus formation are discussed using data analytic methods. These methods aim to handle dataset diversity and scale, and apply data analytics for statistical imputation, estimating future drilling activity, fluids production, and the optimization of water recycling facility locations and size. The objective of this study is to quantify and predict the volumes of produced fluids in the short- and medium-term for the Marcellus shale. The paper accomplishes this objective for the Pennsylvania section comprising 10,000 wells. The application of data analytics to large-scale, data-intensive, low-integrity public environmental databases is illustrated, and challenges of implementation methods are discussed and resolved. In addition, a special class of data analytic tools and workflows for spatiotemporal analysis (spatially correlated variation of parameters with time) is discussed and implemented. The results quantify the prospect of future drilling activity, and water and gas production for all Pennsylvania counties in the Marcellus. Finally, several practical problems of interest on applications of predictive analytics and management support are proposed and solved. The limitations of the proposed workflow are briefly discussed.
KW - Data analytics
KW - Large-scale low-integrity big data problems
KW - Marcellus shale gas
KW - Produced water management
KW - Spatiotemporal analysis
KW - Statistical imputation
UR - http://www.scopus.com/inward/record.url?scp=85057297292&partnerID=8YFLogxK
U2 - 10.1016/j.jngse.2018.11.021
DO - 10.1016/j.jngse.2018.11.021
M3 - Article
AN - SCOPUS:85057297292
VL - 61
SP - 293
EP - 302
JO - Journal of Natural Gas Science and Engineering
JF - Journal of Natural Gas Science and Engineering
SN - 1875-5100
ER -