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  1.  8
    Geomechanical Analysis of Microseismicity in an Organic Shale: A West Virginia Marcellus Shale Example.Erich Zorn, Abhash Kumar, William Harbert & Richard Hammack - 2019 - Interpretation 7 (1):T231-T239.
    Using an innovative workflow incorporating microseismic attributes and geomechanical well logs, we have defined major geomechanical drivers of microseismic expression to understand reservoir stimulation response in an engineering/geologic context. We sampled microseismic data from two hydraulically fractured Marcellus wells in the Appalachian Basin, West Virginia, vertically through the event cloud, crossing shale, limestone, sandstone, and chert. We focused our analysis on the Devonian organic shale and created pseudologs of moment magnitude Mw, b-value, and event count. The vertical moving-average sampling of (...)
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  2.  9
    Characterization of a Fracture Zone Using Seismic Attributes at the In Salah CO2 Storage Project.Rui Zhang, Donald Vasco, Thomas M. Daley & William Harbert - 2015 - Interpretation: SEG 3 (2):SM37-SM46.
    The In Salah carbon dioxide storage project in Algeria has injected more than 3 million tons of carbon dioxide into a water-filled tight-sand formation. During injection, interferometric synthetic aperture radar reveals a double-lobed pattern of up to a 20-mm surface uplift above the horizontal leg of an injection well. Interpretation of 3D seismic data reveals the presence of a subtle linear push-down feature located along the InSAR determined surface depression between the two lobes, which we interpreted to have to be (...)
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    Introduction to Special Section: CO2 Storage and Utilization.Charlotte Sullivan, Alain Bonneville, William Harbert, Neeraj Gupta, Dirk Nieuwland & Joseph Morris - 2015 - Interpretation: SEG 3 (2):SMi-SMi.
  4.  1
    Evaluating Proxies for the Drivers of Natural Gas Productivity Using Machine-Learning Models.William Harbert, Richard Hammack, Erich Zorn, Alexander Bear, Timothy Carr & Abhash Kumar - 2021 - Interpretation 9 (4):SG31-SG46.
    The extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir (...)
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