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  1.  6
    Integrated Geologic, Geophysical, and Petrophysical Data to Construct Full Field Geologic Model of Cambrian-Ordovician and Upper Cretaceous Reservoir Formations, Central Western Sirte Basin, Libya.Abdalla A. Abdelnabi, Yousf Abushalah, Kelly H. Liu & Stephen S. Gao - 2019 - Interpretation: SEG 7 (1):T21-T37.
    The Cambrian-Ordovician and Upper Cretaceous formations, which are the main oil-producing formations in the central Sirte Basin, are structurally complex. The lateral and vertical heterogeneity of the reservoir formations is not well-understood, which negatively affects the performance of the reservoirs. We constructed efficient full-field static models that incorporate the lateral and vertical variation of those reservoir formations by integrating geologic and geophysical data. We determined lithology and reservoir properties by selecting appropriate petrophysical techniques that suit the available well data and (...)
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  2.  7
    Near-Surface Characterization Using Traveltime and Full-Waveform Inversion with Vertical and Horizontal Component Seismic Data.Md Iftekhar Alam - 2019 - Interpretation: SEG 7 (1):T141-T154.
    Seismic imaging of the shallow subsurface can be very challenging when reflections are absent and the data are dominated by ground roll. I analyzed the transmission coda to produce fine-scale, interpretable vertical and horizontal component seismic velocity models using full-waveform inversion. Application of FWI is tested through imaging two buried targets. The first target is a pair of well-documented utility pipes with known diameters and burial depths. The second target is a poorly documented former location of the pipe, which is (...)
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  3.  8
    Use of Seismic Attributes and Open-Hole Log Data to Characterize Production Variability in a Fractured Carbonate Play: A Case Study From Madison County, Texas.Courtney Beck, Anna Khadeeva, Bhaskar Sarmah & Trey Kimbell - 2019 - Interpretation: SEG 7 (1):T167-T178.
    Understanding natural fracture systems is key for tight carbonate plays, in which production is dependent on secondary interconnected porosity networks. Locating geographic areas and stratigraphic sections with high natural fracture density and optimizing well locations and perforations to connect these fractures can enhance well performance and asset value. There is substantial production variation in the Cretaceous stacked carbonate play in East Texas, despite similarities in well completion and perforated intervals. Petrophysical property models did not explain the significant variation in well (...)
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  4.  8
    Hydraulic Fracturing Monitoring: New Concept of Electromagnetics Linked to Elastic Modeling.Ana Curcio & Lucas Macias - 2019 - Interpretation: SEG 7 (1):T39-T48.
    We have developed a simulation of the electromagnetic response and its joint interpretation with fracking monitoring simulation during hydraulic fracturing in an unconventional reservoir. A multiphysics workflow is presented, using a criterion based on a breakdown pressure to generate and propagate the hydraulic fracturing, where the pressure response and EM response were jointly constructed. The approximate solution of Maxwell equations was obtained using a mixed finite-element method combined with a leapfrog time-stepping procedure. The spatial discretization of the Maxwell’s equations is (...)
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  5.  8
    Depositional Model and Controlling Factors of Oolithic Shoal: A Case Study of the Lower Triassic Feixianguan Formation in the Northwestern Sichuan Basin, China.Yuan Huang, Zhenyu Fan, Bing He, Song Tang & Weifeng Du - 2019 - Interpretation: SEG 7 (1):T127-T139.
    Lower Triassic Feixianguan oolithic shoal complexes are widely developed in the northwestern Sichuan Basin, southwest China, where they host large natural gas reserves. To understand their development and the factors that controlled their deposition, we have used observations and interpretations of outcrops, cores, thin sections, well-log data, and seismic data to characterize the geologic and geophysical properties of the oolithic shoals of the Lower Triassic Feixianguan Formation in the Jiange area, northwestern Sichuan Basin. The oolithic shoals of the Feixianguan Formation (...)
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  6.  11
    A Novel Approach to the Quantitative Evaluation of the Mineral Composition, Porosity, and Kerogen Content of Shale Using Conventional Logs: A Case Study of the Damintun Sag in the Bohai Bay Basin, China.Jinbu Li, Shuangfang Lu, Min Wang, Guohui Chen, Weichao Tian & Chenxue Jiao - 2019 - Interpretation: SEG 7 (1):T83-T95.
    The quantitative prediction of the mineral composition, porosity, and kerogen content of shales is significant for the evaluation of shale oil and gas potential and the hydraulic fracturing process. We have developed a new method for the shale’s components prediction by combining the back-propagation neural network and an improved [Formula: see text] method based on conventional logs. First, we constructed and calibrated the shale fraction model according to the volume of the minerals, kerogen, and porosity determined through laboratory analyses. Subsequently, (...)
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  7.  11
    Thin-Bed Detection Using Amplitude Slicing at Minimum Interference Frequency: Method and Application.Changkuan Ni, Guangmin Hu, Huaqin Liu, Mingjun Su & Hailong Ma - 2019 - Interpretation: SEG 7 (1):T113-T126.
    In seismic mapping of reservoirs, it is usually difficult to identify a single layer in a thin interbed formation. The main reason for this is interference of adjacent layers in the thin beds. We have developed a slice method basis spectral decomposition workflow to decrease this interference rely on the good vertical resolution of the thin layers, which is not always achievable. To overcome this, we have developed two frequencies at which the thin-bed response is weakest at a fixed time. (...)
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  8.  7
    Investigation of Seismic Attributes, Depositional Environments, and Hydrocarbon Sweet-Spot Distribution in the Serbin Field, Taylor Formation, Southeast Texas.Osareni C. Ogiesoba, William A. Ambrose & Robert G. Loucks - 2019 - Interpretation: SEG 7 (1):T49-T66.
    We have conducted seismic-attribute analysis at the Serbin field — in an area straddling Lee, Fayette, and Bastrop Counties and covering approximately [Formula: see text] — using new, reprocessed, 3D seismic data to provide additional understanding of depositional environments and better predict the distribution of hydrocarbon sweet spots. We converted the 3D seismic volume into a log-lithology volume and integrated core data to examine the distribution of lithology and interpret depositional environments. By conducting multiattribute analysis, we predicted resistivity volume and (...)
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  9.  4
    The 3D Seismic Characteristics and Significance of the Strike-Slip Faults in the Tazhong Area.RenHai Pu, KunBai Li, Machao Dong, ZiCheng Cao & Pengye Xu - 2019 - Interpretation: SEG 7 (1):T1-T19.
    The eastern part of Tazhong area in the Tarim Basin consists of three sets of vertical strike-slip faults oriented in north–northeast, east–northeast, and west–northwest directions that cut the strata from Cambrian to Carboniferous. The fault belts indicate significant horizon upwarp and downwarp deformations and variations in their stratigraphic thickness on seismic profiles. Through detailed interpretation of the 3D seismic data, we consider that these phenomena reflect the different stress properties and active stages of the faults. The horizon upwarp and downwarp (...)
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  10.  3
    Three-Dimensional Seismic Analysis of Late Paleozoic Coal-Bearing Series Reflections in the Hangjinqi, North Ordos Basin, China.Xiaochuan Wu, Renhai Pu, Donghui Wang, Dong Deng & Machao Dong - 2019 - Interpretation: SEG 7 (1):T67-T82.
    The study of sand bodies and coalbeds that formed during strong events is conducive to understand the relationship between source rocks and reservoirs. Two sets of Late Paleozoic coal-bearing sequences including the Late Carboniferous Taiyuan Formation and Early Permian Shanxi Formation, with an accumulated thickness of more than a half wavelength, were deposited in the Hangjinqi region on the northern margin of the Ordos Basin. These strata appear as two peaks and two troughs, three peaks and three troughs in the (...)
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  11.  12
    Detecting Faults and Channels While Enhancing Seismic Structural and Stratigraphic Features.Xinming Wu & Zhenwei Guo - 2019 - Interpretation: SEG 7 (1):T155-T166.
    A 3D seismic image contains structural and stratigraphic features such as reflections, faults, and channels. When smoothing such an image, we want to enhance all of these features so that they are easier to interpret. Most smoothing methods aim to enhance reflections but may blur faults and channels in the image. A few methods smooth seismic reflections while preserving faults and channel boundaries. However, it has not well-discussed to smooth simultaneously along the seismic reflections and channels, which are linear features (...)
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  12.  12
    The Thickness Imaging of Channels Using Multiple-Frequency Components Analysis.Yan Ye, Bo Zhang, Cong Niu, Jie Qi & Huailai Zhou - 2019 - Interpretation: SEG 7 (1):B1-B8.
    Blending of different frequency components of seismic traces is a common way to estimate the relative time thickness of the formation. Red, blue, and green color blending is one of the most popular blending models in analyzing multiple seismic attributes. Geologists and geophysicist interpreters typically associate low-frequency components with a red color, medium-frequency components with a green color, and high-frequency components with a blue color for the thickness estimation of thin beds using frequency components. However, we found that the same (...)
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  13.  11
    Application of a New Hybrid Particle Swarm Optimization-Mixed Kernels Function-Based Support Vector Machine Model for Reservoir Porosity Prediction: A Case Study in Jacksonburg-Stringtown Oil Field, West Virginia, USA.Zhi Zhong & Timothy R. Carr - 2019 - Interpretation: SEG 7 (1):T97-T112.
    Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. (...)
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