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NARROW
GeoRef Subject
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all geography including DSDP/ODP Sites and Legs
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Africa
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East Africa
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Ethiopia (1)
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Madagascar (1)
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Southern Africa
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Invertebrata
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Primary terms
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Africa
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bibliography (2)
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Ontario
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Western Canada
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carbon
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Cenozoic
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Quaternary
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Holocene (2)
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upper Pleistocene
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Tertiary
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Neogene (1)
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Oligocene (1)
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Western Europe
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plutonic rocks
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diabase (2)
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gabbros (1)
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granites
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A-type granites (1)
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monzogranite (1)
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lamprophyres (1)
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harzburgite (1)
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lherzolite (1)
-
-
-
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porphyry (1)
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andesites (1)
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basalts
-
flood basalts (1)
-
-
dacites (1)
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komatiite (21)
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pyroclastics
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inclusions
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fluid inclusions (1)
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Indian Ocean Islands
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intrusions (15)
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Invertebrata
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Porifera (1)
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isotopes
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radioactive isotopes
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C-14 (1)
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Os-187/Os-186 (1)
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stable isotopes
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C-13/C-12 (3)
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D/H (2)
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Nd-144/Nd-143 (1)
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O-18/O-16 (4)
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Os-187/Os-186 (1)
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Os-188/Os-187 (1)
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Pb-207/Pb-206 (2)
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S-33/S-32 (3)
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S-34/S-32 (6)
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Sr-87/Sr-86 (2)
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maps (2)
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Mesozoic
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Cretaceous
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Jurassic
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Upper Jurassic
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Triassic (1)
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metal ores
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metals
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Sr-87/Sr-86 (2)
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iron (1)
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lead
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Pb-207/Pb-206 (2)
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nickel (1)
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osmium
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Os-188/Os-187 (1)
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metamorphic rocks
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Popocatepetl (1)
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North Cowden field
NORTH COWDEN FIELD, ECTOR COUNTY, TEXAS
North Cowden Field, Ector County, Texas: ABSTRACT
—North-south cross section of producing beds in North Cowden field.
—Regional position of North Cowden field (shown in solid black).
—Areal distribution of “pays” in North Cowden field.
—Northwest-southeast cross section of producing beds in North Cowden field....
Analysis of Typical Salt Water from the North Cowden Field Stanolind Oil a...
Integrated reservoir characterization; beyond tomography
Distribution of Porosity and Permeability in Platform Dolomites: Insight from the Permian of West Texas
—Map showing the location of North Riley and South Cowden fields on the Cen...
Recent Developments in the South Mid-Continent
Abstract Geostatistical-simulation techniques are increasingly being used to create heterogeneous realizations for flow modeling and to assess uncertainty in hydrocarbon resources and reserves. These geostatistical-simulation techniques reproduce the input statistics within ergodic fluctuations. The input statistics representing various model parameters must be computed from data that are representative of the entire domain being modeled. Geostatistical simulation does not accommodate a lack of representativeness in the data. Moreover, the extent to which the input statistics are reproduced depends almost exclusively on the size of the modeling domain relative to the range of spatial correlation; fluctuations in realizations of the full reservoir model do not depend entirely on the uncertainty of the input statistics. It is necessary to explicitly incorporate the uncertainty of the input statistics because they have a much larger and more realistic impact on the uncertainty of the full reservoir model than stochastic fluctuations. The best practices for determining representative input values of model parameters and quantification of their uncertainty are presented in this chapter.
Constraining Geostatistical Reservoir Descriptions with 3-D Seismic Data to Reduce Uncertainty
Abstract The geostatistical external drift method is used to integrate three-dimensional (3-D) seismic data into a reservoir description and is illustrated with an application on a west Texas Permian basin interbedded carbonate-clastic reservoir. Seismic reflection amplitude, inverted to acoustic impedance, supplements sparse well control to estimate interwell porosity. The North Cowden unit is a mature field and serves as a laboratory for many reservoir characterization experiments. The extensive wireline and core database available for the area covered by a high-resolution 3-D seismic survey was resampled to mimic scenarios similar to three stages in a reservoir’s life: (1) a late exploration/appraisal phase; (2) a development phase; and (3) a mature production phase, typical of many Permian basin fields. Spatial interpolation by kriging porosity with and without seismic data are compared. Stochastic (Monte Carlo) simulations are used to evaluate interpolation uncertainty (standard error). Interpolation uncertainty is greatly reduced when seismic data are integrated into the reservoir description.
Abstract Several factors will cause oil and gas producers to focus increasingly on characterization of petroleum reservoirs in the near future. Experts predict that more than 95% of the world's oil production in the 21st century will come from existing fields. Recovery rates from present producing fields are often quite modest. Much less than 50% of available oil usually is produced from a reservoir. Moreover, there is still an insatiable thirst for oil and gas consumption among the world's population. Petroleum is also necessary for the production of many chemicals and plastics. All these factors impact the need for enhanced recovery of petroleum. Increased production will be made possible only through effective reservoir characterization. Reservoir characterization is a multidisciplinary field that attempts to describe petroleum deposits and the nature of the rocks that contain hydrocarbons. Reservoir characterization relies on expertise from petroleum engineering, geology, and geophysics. The integration of information from these fields focuses on various aspects of the reservoir. The formation of reservoir-characterization teams requires the synergistic teamwork of engineers, geologists, and geophysicists. Books that describe this emerging science include those by Sheriff (1992) and by Gadallah (1994) . The fundamental goals of reservoir characterization are to determine the following: presence of hydrocarbons reservoir porosity reservoir permeability Several factors will cause oil and gas producers to focus increasingly on characterization of petroleum reservoirs in the near future. Experts predict that more than 95% of the world's oil production in the 21st century will come