15 found
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  1.  12
    Seismic Reservoir Characterization of Utica-Point Pleasant Shale with Efforts at Quantitative Interpretation — A Case Study: Part 1.Satinder Chopra, Ritesh Kumar Sharma, Hossein Nemati & James Keay - 2018 - Interpretation: SEG 6 (2):T313-T324.
    The Utica Shale is one of the major source rocks in Ohio, and it extends across much of the eastern United States. Its organic richness, high content of calcite, and development of extensive organic porosity make it a perfect unconventional play, and it has gained the attention of the oil and gas industry. The primary target zone in the Utica Play includes the Utica Formation, Point Pleasant Formation, and Trenton Formation intervals. We attempt to identify the sweet spots within the (...)
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  2.  13
    Seismic Reservoir Characterization of Utica-Point Pleasant Shale with Efforts at Quantitative Interpretation — A Case Study: Part 1.Satinder Chopra, Ritesh Kumar Sharma, Hossein Nemati & James Keay - 2018 - Interpretation 6 (2):T313-T324.
    The Utica Shale is one of the major source rocks in Ohio, and it extends across much of the eastern United States. Its organic richness, high content of calcite, and development of extensive organic porosity make it a perfect unconventional play, and it has gained the attention of the oil and gas industry. The primary target zone in the Utica Play includes the Utica Formation, Point Pleasant Formation, and Trenton Formation intervals. We attempt to identify the sweet spots within the (...)
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  3.  23
    Seismic Reservoir Characterization of Duvernay Shale with Quantitative Interpretation and Induced Seismicity Considerations — A Case Study.Satinder Chopra, Ritesh Kumar Sharma, Amit Kumar Ray, Hossein Nemati, Ray Morin, Brian Schulte & David D’Amico - 2017 - Interpretation: SEG 5 (2):T185-T197.
    The Devonian Duvernay Formation in northwest central Alberta, Canada, has become a hot play in the past few years due to its richness in liquid and gaseous hydrocarbon resources. The oil and gas generation in this shale formation made it the source rock for many oil and gas fields in its vicinity. We attempt to showcase the characterization of Duvernay Formation using 3D multicomponent seismic data and integrating it with the available well log and other relevant data. This has been (...)
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  4.  9
    Seismic Reservoir Characterization of Utica-Point Pleasant Shale with Efforts at Fracability Evaluation — Part 2: A Case Study.Ritesh Kumar Sharma, Satinder Chopra, James Keay, Hossein Nemati & Larry Lines - 2018 - Interpretation: SEG 6 (2):T325-T336.
    The Utica Formation in eastern Ohio possesses all the prerequisites for being a successful unconventional play. Attempts at seismic reservoir characterization of the Utica Formation have been discussed in part 1, in which, after providing the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, and building of robust low-frequency models for prestack simultaneous impedance inversion were explained. All these efforts were aimed at identification of sweet spots in the Utica Formation in terms of (...)
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  5.  13
    Seismic Reservoir Characterization of Utica-Point Pleasant Shale with Efforts at Fracability Evaluation- a Case Study: Part 2.Ritesh Kumar Sharma, Satinder Chopra, James Keay, Hossein Nemati & Larry Lines - forthcoming - Interpretation:1-36.
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  6.  1
    Seismic Reservoir Characterization of the Gassum Formation in the Stenlille Aquifer Gas Storage, Denmark — Part 1.Satinder Chopra, Ritesh Kumar Sharma, Kenneth Bredesen & Kurt J. Marfurt - 2022 - Interpretation 10 (4):T693-T705.
    Seismic reservoir characterization plays an important role in carbon capture and storage analysis. The Havnsø anticlinal structure in Denmark is a prospective CO2 storage site due to its proximity to two large emission sources—a coal-fired power station and a nearby refinery. Although legacy 2D seismic lines over the area outline the anticlinal structure, their quality is insufficient for quantitative interpretation. Earlier studies have shown that the natural gas stored in the Stenlille aquifer exhibits a seismic response similar to the modeled (...)
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  7.  2
    Replacing Conventional Brittleness Indices Determination with New Attributes Using True Hydrofracturing Mechanism.Ritesh Kumar Sharma, Satinder Chopra & Larry R. Lines - 2020 - Interpretation 8 (4):T1081-T1095.
    Shale resource plays are associated with low permeability; hence, hydraulic fracturing is required for their stimulation and production. Even though considerable nonuniqueness exists in identifying favorable zones for hydraulic fracturing, geophysicists seem to be avid followers of low-Poisson’s ratio and high-Young’s modulus brittleness criteria, proposed a decade ago. We highlight the misinterpretation that one may run into in following such a criterion for any shale play and develop a new attribute that makes use of strain energy density and fracture toughness. (...)
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  8.  2
    Seismic Reservoir Characterization of Bone Spring and Wolfcamp Formations in the Delaware Basin — A Case Study: Part 1.Satinder Chopra, Ritesh Kumar Sharma & James Keay - 2020 - Interpretation 8 (4):T927-T940.
    The Delaware and Midland Basins are multistacked plays with production being drawn from different zones. Of the various prospective zones in the Delaware Basin, the Bone Spring and Wolfcamp Formations are the most productive and thus are the most drilled zones. To understand the reservoirs of interest and identify the hydrocarbon sweet spots, a 3D seismic inversion project was undertaken in the northern part of the Delaware Basin in 2018. We have examined the reservoir characterization exercise for this dataset in (...)
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  9.  1
    Reservoir Characterization Over the Lille Prinsen and Ivar Aasen Fields in the Norwegian North Sea Using Ocean-Bottom-Node Seismic Data — A Case Study.Satinder Chopra, Ritesh Kumar Sharma, Mikal Trulsvik, Adriana Citlali Ramirez, David Went & Bent Erlend Kjølhamar - 2021 - Interpretation 9 (3):T969-T985.
    We have developed an integrated workflow for estimating elastic parameters within the Late Triassic Skagerrak Formation, the Middle Jurassic Sleipner and Hugin Formations, the Paleocene Heimdal Formation, and the Eocene Grid Formation in the Utsira High area of the Norwegian North Sea. Our workflow begins with petrophysical analysis carried out at the available wells. Then, model-based prestack simultaneous impedance inversion outputs were derived, and attempts were made to estimate the petrophysical parameters from seismic data using extended elastic impedance. On not (...)
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  10. Improving Porosity and Gamma-Ray Prediction for the Middle Jurassic Hugin Sandstones in the Southern Norwegian North Sea with the Application of Deep Neural Networks.Satinder Chopra, Ritesh Kumar Sharma, Kurt J. Marfurt, Rongfeng Zhang & Renjun Wen - 2022 - Interpretation 10 (1):T25-T34.
    The complete characterization of a reservoir requires accurate determination of properties such as the porosity, gamma ray, and density, among others. A common workflow is to predict the spatial distribution of properties measured by well logs to those that can be computed from the seismic data. In general, a high degree of scatter of data points is seen on crossplots between P-impedance and porosity, or P-impedance and gamma ray, suggesting great uncertainty in the determined relationship. Although for many rocks there (...)
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  11. Seismic Reservoir Characterization of the Gassum Formation in the Stenlille Aquifer Gas Storage, Denmark: Part – 2, Unsupervised Classification.Satinder Chopra, Ritesh Kumar Sharma, Kenneth Bredesen, Thang Ha & Kurt J. Marfurt - forthcoming - Interpretation:1-43.
    Ideally, a good static reservoir model provides an accurate estimate of the extent, porosity, permeability, and lithology of the container as well as the properties of the seal and any faults or fractures that may allow the reservoir to leak. The major pitfall of deterministic, statistical, or supervised learning workflows is that they estimate only the properties sampled by the wells or provided by empirical relations and may miss mapping heterogeneities in the reservoir and seal that can give rise to (...)
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  12. Seismic Reservoir Characterization of the Gassum Formation in the Stenlille Aquifer Gas Storage, Denmark — Part 2: Unsupervised Classification.Satinder Chopra, Ritesh Kumar Sharma, Kenneth Bredesen, Thang Ha & Kurt J. Marfurt - 2022 - Interpretation 10 (4):T749-T758.
    Ideally, a good static reservoir model provides an accurate estimate of the extent, porosity, permeability, and lithology of the container as well as the properties of the seal and any faults or fractures that may allow the reservoir to leak. The major pitfall of deterministic, statistical, or supervised learning workflows is that they estimate only the properties sampled by the wells or provided by empirical relations and may miss mapping heterogeneities in the reservoir and seal that can give rise to (...)
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  13.  18
    Characterization of Shallow High-Amplitude Seismic Anomalies in the Hoop Fault Complex, Barents Sea.Satinder Chopra, Ritesh Kumar Sharma, Graziella Kirtland Grech & Bent Erlend Kjølhamar - 2017 - Interpretation: SEG 5 (4):T607-T622.
    The shallow migrating hydrocarbon fluids in the western Barents Sea are usually found to be associated with high seismic amplitudes. We have attempted to characterize such shallow high-amplitude anomalies in the Hoop Fault Complex area of the western Barents Sea. The workflow is devised for discrimination of anomalies that are associated with the presence of hydrocarbons from those that are not, and quantifying them further includes the computation of a set of seismic attributes and their analyses. These attributes comprise coherence, (...)
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  14.  4
    A Robust Workflow for Performing Joint Impedance Inversion and its Applications.Ritesh Kumar Sharma, Satinder Chopra & Larry R. Lines - 2020 - Interpretation 8 (1):T141-T149.
    Multicomponent seismic data offer several advantages for characterizing reservoirs with the use of the vertical component and mode-converted data. Joint impedance inversion inverts both of these data sets simultaneously; hence, it is considered superior to simultaneous impedance inversion. However, the success of joint impedance inversion depends on how accurately the PS data are mapped on the PP time domain. Normally, this is attempted by performing well-to-seismic ties for PP and PS data sets and matching different horizons picked on PP and (...)
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  15.  3
    Seismic Reservoir Characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and Uncertainty in Characterization Using Rock Physics — A Case Study: Part 2.Ritesh Kumar Sharma, Satinder Chopra & Larry Lines - 2020 - Interpretation 8 (4):T1057-T1069.
    The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where (...)
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