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Methods and systems for creating a numerical pseudocore model, comprising: a) obtaining logging data from a reservoir having depth-defined intervals of the reservoir, and processing the logging data into interpretable borehole image data having unidentified borehole image data; b) examining one of t

Methods and systems for creating a numerical pseudocore model, comprising: a) obtaining logging data from a reservoir having depth-defined intervals of the reservoir, and processing the logging data into interpretable borehole image data having unidentified borehole image data; b) examining one of the interpretable borehole image data, other processed logging data or both to generate the unidentified borehole image data, processing the generated unidentified borehole image data into the interpretable borehole image data to generate warped fullbore image data; c) collecting one of a core from the reservoir, the logging data or both and generating a digital core data from one of the collected core, the logging data or both such that generated digital core data represents features of one or more depth-defined interval of the reservoir; and d) processing generated digital core data, interpretable borehole image data or the logging data to generate realizations of the numerical pseudocore model.

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1. A method for creating a 3-dimensional numerical pseudocore model comprising: a) obtaining logging data from a reservoir that includes depth-defined intervals of the reservoir, and processing the logging data into a portion of at least one interpretable borehole image data having unidentified bore

1. A method for creating a 3-dimensional numerical pseudocore model comprising: a) obtaining logging data from a reservoir that includes depth-defined intervals of the reservoir, and processing the logging data into a portion of at least one interpretable borehole image data having unidentified borehole image data;b) examining one of the portion of the at least one interpretable borehole image data, the processed logging data or both to generate the unidentified borehole image data, then processing the generated unidentified borehole image data utilizing a multi-point statistics (MPS) algorithm FILTERSIM into the portion of the at least one interpretable borehole image data so as to generate fullbore image data;c) collecting at least one core from the reservoir and generating 3-dimensional digital core data from the collected at least one core sample using a computed-tomographic scan (CTscan), the generated digital core data representing one of features or structures of one or more depth-defined interval of the reservoir;d) pre-modeling the generated digital core data to define 3-dimensional sizes and shapes of petrophysical facies;e) using a multi-point statistics (MPS) algorithm SNESIM to generate realizations of numerical pseudocores for two or more petrophysical facies, and conditioning the realizations to match petrophysical facies sizes and shapes observed in the generated fullbore image data and the generated pre-modeled 3-dimensional digital core data; and(f) resampling the numerical pseudocores of step (e) to a radial grid, wherein the radial grid provides for a flow investigation of the numerical pesudocore model and includes building a cylindrical grid having one of one or more shapes or one or more layers, resampling the numerical pseudocore model from Cartesian coordinates, each voxel of which has a constant porosity or a constant permeability according to an associated rock type of the voxel, to cylindrical coordinates, wherein each cylindrical cell includes multiple Cartesian voxels of the numerical pseudocore model. 2. The method of claim 1, wherein the portion of the at least one interpretable borehole image data is a training image that is two-dimensional (2D) scalar arrays of continuously variable numerical values. 3. The method of claim 1, wherein the portion of the at least one interpretable borehole image data comprises processed raw data that includes measured values and non-measured values. 4. The method of claim 1, wherein the unidentified borehole image data represents one of non-measured values of the portion of the at least one interpretable borehole image data or data gaps in the portion of the at least one interpretable borehole image data. 5. The method of claim 4, wherein the data gaps are from the group consisting of one of at least one damaged pad in the reservoir, at least one damaged area in the reservoir, at least one pad with inadequate pad pressure against a borehole wall in the reservoir, at least one pad obstructed from contacting the borehole wall in the reservoir or at least one inoperable pad in the reservoir. 6. The method of claim 1, wherein the other processed logging data is from the group consisting of one of logging-while-drilling data or wireline logging data, or some combination thereof. 7. The method of claim 6, wherein the wireline logging data includes at least one of gamma ray, density, sonic, neutron, caliper or resistivity logs. 8. The method of claim 1, wherein the at least one fullbore image data is one of two-dimensional (2D), three-dimensional (3D) or both. 9. The method of claim 1, wherein the generating at least one fullbore image consists of: 1) selecting a depth-defined interval of a borehole image log from the depth-defined intervals of the reservoir, processing the depth-defined interval of the borehole image log utilizing the multi-point statistics (MPS) algorithm FILTERSIM, wherein the MPS algorithm provides for a modeling capturing unidentified geological structures from the portion of the at least one interpretable borehole image data so as to identify data locations in the portion of the at least one interpretable borehole image data;2) processing the identified data locations using filter scores to group and then simulate patterns in the unidentified borehole image data whereby the unidentified borehole image data represents one of non-measured values of the portion of the at least one interpretable borehole image or data gaps in the portion of the at least one interpretable borehole image;3) identifying petrophysical facies of the reservoir, wherein the petrophysical facies is from the group consisting of one of fractures, pores, vugs, conductive patches of rocks in a borehole wall of the reservoir, resistive patches of rocks in the borehole wall of the reservoir, bed boundaries or a rock matrix; and4) processing the unidentified borehole image data into the portion of the at least one interpretable borehole image so as to generate the at least one fullbore image, then with a known borehole diameter warping the at least one fullbore image into an original three-dimensional (3D) shape whereby the numerical pseudocore is conditioned so as to match one of the warped portion of the fullbore image or the portion of the at least one interpretable borehole image. 10. The method of claim 1, wherein the logging data is from the group consisting of one of logging data having multiple depths of investigation, analog models of core data from one or more reservoirs or some combination thereof. 11. The method of claim 1, wherein the 3-dimensional sizes and shapes of the petrophysical facies are from the group consisting of one of fractures, pores, vugs, porous patches of rocks in a borehole wall of the reservoir, electrically conductive patches of rocks in the borehole wall of the reservoir, electrically resistive patches of rocks in the borehole wall of the reservoir, bed boundaries or a rock matrix. 12. The method of claim 11, wherein pre-modeling the generated digital core data includes simulating via dilation of other petrophysical facies of the collected at least one core by a fixed number of voxels. 13. The method of claim 1, wherein the at least one core is collected from at least one other reservoir. 14. The method of claim 1, wherein a second core from the at least one core is obtained from one or more reservoirs. 15. The method of claim 1, wherein step (e) includes plotting a digital file of the generated realizations of the numerical pseudocore model onto one of a digital media or hard copy media. 16. The method of claim 1, wherein the radial grid provides for a flow investigation of the numerical pseudocore model and includes: 1) assigning each cylindrical cell in the cylindrical grid an average porosity and an average permeability based on the included multiple Cartesian voxels; and2) assigning each cylindrical cell in the cylindrical grid relative permeability and capillary pressure curves based on the dominant rock type of the included multiple Cartesian voxels, using a reference table of one of capillary pressure, relative permeability or both for different facies in the numerical pseudocore that is fed into a flow simulator, whereby to quantify an impact of rock heterogeneity on fluid flow based on the pseudocore model referring to one of special core analysis (SCAL), mercury injection capillary pressure (MICP) data, pore network modeling or some combination thereof, of one or more core samples having the same rock type. 17. The method of claim 16, wherein the associated rock type is from the group consisting of one of fractures, pores, vugs, conductive patches of rocks in a borehole wall of the reservoir, resistive patches of rocks in the borehole wall of the reservoir, bed boundaries or a rock matrix. 18. The method of claim 1, further comprising: g) performing flow simulations of one or more geographically associated reservoirs. 19. The method of claim 18, wherein performing flow simulations includes one of a single phase or multiphase flow, wherein the flow simulation is performed on the calculated numerical pseudocore model of step (f) so as to estimate one or more parameters. 20. The method of claim 19, wherein the one or more parameters are from the group consisting of porosity, permeability, capillary pressure, relative permeability, water cut, oil recovery factor or recovery efficiency. 21. The method of claim 18, further comprising: conducting a sensitivity analysis on flow-related parameters, so as to determine a process for one of water flooding, gas flooding, steam flooding or enhanced oil recovery (EOR). 22. The method of claim 21, wherein the flow-related parameters are from the group consisting of porosity, permeability, capillary pressure, relative permeability, water cut, oil recovery factor or recovery efficiency. 23. The method of claim 18, further comprising: determining a process for evaluating wettability effects in estimated parameters, wherein the estimated parameters includes one of capillary pressure, relative permeability, water cut, oil recovery factor or recovery efficiency. 24. The method of claim 1, further comprising: performing flow simulations on the radial grid. 25. A method for creating a 3-dimensional numerical pseudocore model comprising: a) obtaining logging data from a reservoir that includes depth-defined intervals of the reservoir, and processing the logging data into a portion of at least one interpretable borehole image data;b) examining one of the portion of the at least one interpretable borehole image data, the processed logging data or both to generate at least one full-bore image data;c) collecting at least one core from the reservoir and generating a digital core data using a computed-tomographic scan (CTscan) from the collected at least one core, the generated digital core data representing one of features or structures of one or more depth-defined interval of the reservoir;d) pre-modeling the generated digital core data, to define the 3-dimensional sizes and shapes of the petrophysical facies;e) using a multi-point statistics (MPS) algorithm SNESIM to generate realizations of numerical pseudocores for two or more petrophysical facies, and conditioning the realizations to match petrophysical facies sizes and shapes observed in the generated fullbore image data and the generated pre-modeled digital core data; andf) resampling the numerical pseudocores of step (e) to a radial grid, wherein the radial grid provides for a flow investigation of the numerical pesudocore model and includes building a cylindrical grid having one of one or more shapes or one or more layers, resampling the numerical pseudocore model from Cartesian coordinates, each voxel of which has a constant porosity or a constant permeability according to an associated rock type of the voxel, to cylindrical coordinates, wherein each cylindrical cell includes multiple Cartesian voxels of the numerical pseudocore model. 26. The method of claim 25, wherein the portion of the at least one interpretable borehole image data is a training image that is two-dimensional (2D) scalar arrays of continuously variable numerical values. 27. The method of claim 25, wherein the other processed logging data is from the group consisting of one of logging-while-drilling data or wireline logging data, or some combination thereof. 28. The method of claim 25, wherein the logging data is from the group consisting of one of logging data having multiple depths of investigation or analog models of core data from one or more reservoirs or some combination thereof. 29. The method of claim 25, wherein the 3-dimensional sizes and shapes of the petrophysical facies are from the group consisting of one of fractures, pores, vugs, porous patches of rocks in a borehole wall of the reservoir, electrically conductive patches of rocks in the borehole wall of the reservoir, electrically resistive patches of rocks in the borehole wall of the reservoir, bed boundaries or a rock matrix. 30. The method of claim 29, wherein pre-modeling the generated digital core data includes simulating via dilation of other petrophysical facies of the collected at least one core by a fixed number of voxels. 31. A method for creating a 3-dimensional numerical pseudocore model comprising: a) obtaining logging data from a reservoir that includes depth-defined intervals of the reservoir, and processing the logging data into a portion of at least one interpretable borehole image data having unidentified borehole image data;b) examining one of the portion of the at least one interpretable borehole image data, the processed logging data or both to generate the unidentified borehole image data, then processing the generated unidentified borehole image data utilizing a multi-point statistics (MPS) algorithm into the portion of the at least one interpretable borehole image data so as to generate at least one warped fullbore image data;c) collecting at least one core from the reservoir, and generating 3-dimensional core data from the collected at least one core using a computed-tomographic scan (CTscan), the generated digital core data representing one of features or structures of one or more depth-defined intervals of the reservoir;d) pre-modeling a portion of the generated 3-dimensional digital core data, the portion of at least one interpretable borehole image data, portion of the logging data or some combination thereof, to generate realizations of the numerical pseudocore model;wherein the generating of realization of the numerical pseudocore model includes using a multi-point statistics (MPS) algorithm SNESIM to generate realizations of numerical pseudocores for two or more facies, so as to condition the realizations to match facies sizes and shapes observed in the portion of the at least one interpretable borehole image of the portion of the generated 3-dimensional digital core data and the generated at least one warped fullbore image data; andresampling the numerical pseudocores to a radial grid, wherein the radial grid provides for a flow investigation of the numerical pesudocore model and includes building a cylindrical grid having one of one or more shapes or one or more layers, resampling the numerical pseudocore model from Cartesian coordinates, each voxel of which has a constant porosity or a constant permeability according to an associated rock type of the voxel, to cylindrical coordinates, wherein each cylindrical cell includes multiple Cartesian voxels of the numerical pseudocore model.