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• W1508706384 abstract "is necessary for understanding well performance and addressing problems observed by the operators. The purpose of this work is to establish relationships among permeability, geophysical and other data by integrating geologic, geophysical and engineering data into an interdisciplinary quantification of reservoir heterogeneity as it relates to production. Conventional stratigraphic correlation and core description shows that the Gordon sandstone is composed of three parasequences, formed along the Late Devonian shoreline of the Appalachian Basin. The parasequences comprise five lithofacies, of which one includes reservoir sandstones. Pay sandstones were found to have permeabilities in core ranging from 10 to 200 mD, whereas non-pay sandstones have permeabilities ranging from below the level of instrumental detection to 5 mD; Conglomeratic zones could take on the permeability characteristics of enclosing materials, or could exhibit extremely low values in pay sandstone and high values in non-pay or low permeability pay sandstone. Four electrofacies based on a linear combination of density and scaled gamma ray best matched correlations made independently based on visual comparison of geophysical logs. Electrofacies 4 with relatively high permeability (mean value > 45 mD) was determined to be equivalent to the pay sandstone within the Gordon reservoir. Three-dimensional models of the electrofacies in the pilot waterflood showed that electrofacies 4 is present throughout this area, and the other electrofacies are more disconnected. A three-layer, back-propagation artificial neural network with three slabs in the middle layer can be used to predict permeability and porosity from gamma ray and bulk density logs, the first and the second derivatives of the log data with respect to depth, well location, and log baselines. Two flow units were defined based on the stratigraphic model and geophysical logs. A three-dimensional reservoir model including the flow units, values of permeability calculated through the artificial neural network and injection pressure-rate information were then used as inputs for a reservoir simulator to predict oil production performance for the center producers in the pilot area. This description of the reservoir provided significantly better simulation results than earlier results obtained using simple reservoir models. Bulk density and gamma ray logs were used to identify flow units throughout the field. As predicted by the stratigraphic analysis, one of the flow units crosses stratigraphic units in the reservoir. A neural network was used to predict permeability values for each flow unit in producer and injection wells. The reservoir simulator was utilized to predict the performance of two flood patterns located to the north of the pilot area. Considering the simple model utilized for simulation, the results are in very good agreement with the field history." @default.
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• W1508706384 date "2002-05-21" @default.
• W1508706384 modified "2023-09-26" @default.
• W1508706384 title "Reservoir Characterization of Upper Devonian Gordon Sandstone, Jacksonburg, Stringtown Oil Field, Northwestern West Virginia" @default.
• W1508706384 doi "https://doi.org/10.2172/794278" @default.
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