A data analytic workflow to forecast produced water from Marcellus shale

Amin Ettehadtavakkol, Ali Jamali

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish
Pages (from-to)293-302
Number of pages10
JournalJournal of Natural Gas Science and Engineering
StatePublished - Jan 2019


  • Data analytics
  • Large-scale low-integrity big data problems
  • Marcellus shale gas
  • Produced water management
  • Spatiotemporal analysis
  • Statistical imputation


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