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  1.  29
    Estimation of Total Organic Carbon and Brittleness Volume.Sumit Verma, Tao Zhao, Kurt J. Marfurt & Deepak Devegowda - 2016 - Interpretation: SEG 4 (3):T373-T385.
    The Barnett Shale in the Fort Worth Basin is one of the most important resource plays in the USA. The total organic carbon and brittleness can help to characterize a resource play to assist in the search for sweet spots. Higher TOC or organic content are generally associated with hydrocarbon storage and with rocks that are ductile in nature. However, brittle rocks are more amenable to fracturing with the fractures faces more resistant to proppant embedment. Productive intervals within a resource (...)
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  2.  1
    Mississippian Meramec Lithologies and Petrophysical Property Variability, Stack Trend, Anadarko Basin, Oklahoma.Michael J. Miller, Matthew J. Pranter, Ishank Gupta, Deepak Devegowda, Kurt J. Marfurt, Carl Sondergeld, Chandra Rai, Chris T. McLain, James Larese & Richard E. Packwood - 2021 - Interpretation 9 (2):SE1-SE21.
    Mississippian Meramec reservoirs of the Sooner Trend in the Anadarko in Canadian and Kingfisher Counties play are comprised of silty limestones, calcareous siltstones, argillaceous calcareous siltstones, argillaceous siltstones, and mudstones. We found that core-defined reservoir lithologies are related to petrophysics-based rock types derived from porosity-permeability relationships using a flow-zone indicator approach. We classified lithologies and rock types in noncored wells using an artificial neural network with overall accuracies of 93% and 70%, respectively. We observed that mudstone-rich rock type 1 exhibits (...)
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  3.  11
    Integration of Surface Seismic, Microseismic, and Production Logs for Shale Gas Characterization: Methodology and Field Application.John Henry Alzate & Deepak Devegowda - 2013 - Interpretation: SEG 1 (2):SB37-SB49.
    Technologies such as horizontal drilling and multistage hydraulic fracturing are central to ensuring the viability of shale oil and gas resource development by maximizing contact with the most productive reservoir volumes. However, characterization efforts based on the use of well logs and cores, although very informative, may be associated with substantial uncertainty in interwell volumes. Consequently, this work is centered around the development of a predictive tool based on surface seismic data analysis to rapidly demarcate the most prolific reservoir volumes, (...)
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  4.  18
    Introduction to Special Section: Pattern Recognition and Machine Learning.Vikram Jayaram, Per Age Avseth, Kostia Azbel, Theirry Coléou, Deepak Devegowda, Paul de Groot, Dengliang Gao, Kurt Marfurt, Marcilio Matos, Tapan Mukerji, Manuel Poupon, Atish Roy, Brian Russell, Brad Wallet & Vikas Kumar - 2015 - Interpretation: SEG 3 (4):SAEi-SAEii.
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  5. Quantifying the Sensitivity of Seismic Facies Classification to Seismic Attribute Selection: An Explainable Machine-Learning Study.David Lubo-Robles, Deepak Devegowda, Vikram Jayaram, Heather Bedle, Kurt J. Marfurt & Matthew J. Pranter - 2022 - Interpretation 10 (3):SE41-SE69.
    During the past two decades, geoscientists have used machine learning to produce a more quantitative reservoir characterization and discover hidden patterns in their data. However, as the complexity of these models increases, the sensitivity of their results to the choice of the input data becomes more challenging. Measuring how the model uses the input data to perform either a classification or regression task provides an understanding of the data-to-geology relationships which indicates how confident we are in the prediction. To provide (...)
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  6.  19
    Rock Typing in the Upper Devonian – Lower Mississippian Woodford Shale Formation, Oklahoma, Usa.Ishank Gupta, Chandra Rai, Carl Sondergeld & Deepak Devegowda - 2018 - Interpretation: SEG 6 (1):SC55-SC66.
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    Machine Learning Regressors and Their Metrics to Predict Synthetic Sonic and Mechanical Properties.Ishank Gupta, Deepak Devegowda, Vikram Jayaram, Chandra Rai & Carl Sondergeld - 2019 - Interpretation 7 (3):SF41-SF55.
    Planning and optimizing completion design for hydraulic fracturing require a quantifiable understanding of the spatial distribution of the brittleness of the rock and other geomechanical properties. Eventually, the goal is to maximize the stimulated reservoir volume with minimal cost overhead. The compressional and shear velocities can also be used to calculate Young’s modulus, Poisson’s ratio, and other mechanical properties. In the field, sonic logs are not commonly acquired and operators often resort to regression to predict synthetic sonic logs. We have (...)
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  8.  2
    Rock Typing in the Upper Devonian-Lower Mississippian Woodford Shale Formation, Oklahoma, USA.Ishank Gupta, Chandra Rai, Carl Sondergeld & Deepak Devegowda - 2018 - Interpretation 6 (1):SC55-SC66.
    Most U.S. shale plays are spatially extensive with regions of different thermal maturity and varying production prospects. With increasing understanding of the heterogeneity, microstructure, and anisotropy of shales, efforts are now directed to identifying the sweet spots and optimum completion zones in any shale play. Rock typing is a step in this direction. We have developed an integrated workflow for rock typing using laboratory-petrophysical measurements on core samples and well logs. A total of seven wells with core data were considered (...)
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    Stratigraphic Variability of Mississippian Meramec Chemofacies and Petrophysical Properties Using Machine Learning and Geostatistical Modeling, STACK Trend, Anadarko Basin, Oklahoma.Laynie Hardisty, Matthew J. Pranter, Deepak Devegowda, Kurt J. Marfurt, Carl Sondergeld, Chandra Rai, Ishank Gupta, Heyleem Han, Son Dang, Chris T. McLain & Richard E. Larese - 2021 - Interpretation 9 (3):T987-T1007.
    Mississippian Meramec deposits and reservoirs in the Sooner Trend in the Anadarko in Canadian and Kingfisher counties play of Oklahoma are comprised of silty limestones, calcareous sandstones, argillaceous-calcareous siltstones, argillaceous siltstones, and mudstones. We used core-derived X-ray fluorescence data and established environmental proxies to evaluate the occurrence of specific elements and to illustrate their stratigraphic variability. For the Mississippian Meramec, six indicator elements or element ratios serve as proxies for clay, detrital sediment, carbonate deposits, calcite cement, and quartz. We used (...)
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  10.  1
    Introduction to Special Section: Insights to Digital Oilfield Data Using Artificial Intelligence and Big Data Analytics.Vikram Jayaram, Atish Roy, Bill Barna, Deepak Devegowda, Jacqueline Floyd, Pradeepkumar Ashok, Aria Abubakar, Anisha Kaul & Emmanuel Schnetzler - 2019 - Interpretation 7 (3):SFi-SFi.
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