Abstract: Methods for characterizing a geological formation, the methods include retrieving measured data provided by a measuring tool along one or more logged borehole length for a borehole, another borehole or both in order to produce a borehole imaging log. Selecting depth-defined intervals of the borehole imaging log as training images for inputting in a multi-point geostatistical model. Determining pattern based simulations for each training image using a pixel-based template of the multi-point geostatistical model so as to obtain training image patterns. Using the pattern based simulation of each training image to assign to each of the training image a corresponding training image pattern. Constructing from the training image patterns one or more fullbore image log of a borehole wall of the borehole. Repeat the second to fourth steps through the one or more logged borehole length in order to construct fullbore images from successive, adjacent training images.

Abstract: A method is disclosed for determining wettability in a subsurface reservoir. Embodiments of the method utilize image analysis techniques for determining wettability indicator values. Wettability indicator values may be determined based on a hydrocarbon wet fraction and a water wet fraction of pore linings, which may both be determined by image analysis of pore walls lined with hydrocarbons using images of sections obtained from core samples. Further details and advantages of various embodiments of the method are described in more detail herein.

Abstract: Methods for upscaling digital rock modeling data are described. Core-plug samples for pore-scale modeling are chosen using whole-core mini-permeability grids and conventional CT scans. Pore models or pore-network models are used for flow modeling. Borehole-scale models use MPS (Multi-Point Statistics) to combine mini-permeability grids and conventional CTscans of whole core with electrical borehole images to create 3D numerical pseudocores for each RRT (Reservoir Rock Type). Effective SCAL properties computed from various MPS borehole-scale realizations or models are used to populate interwell-scale models for each RRT. Effective properties computed from flow simulations for interwell volumes are used to populate full-field scale models. At the full-field scale, outcrop analogs, sequence stratigraphy, forward stratigraphic models, diagenetic models, and basin-scale models are combined using MPS to improve flow simulations.

Abstract: 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.

Abstract: Methods for characterizing a sample of porous media using a measuring device along with a multipoint statistical (MPS) model. Retrieving a set of reflected measured data provided by the measuring device of a surface of the sample in order to produce a sample imaging log, wherein the retrieved set of measured data is communicated to a processor. Selecting depth-defined surface portions of the sample from the sample imaging log as a training image for inputting in the MPS model. Determining pattern based simulations from the training image using one of a pixel-based template which is applied to the training image. Constructing from the pattern based simulations a complete-sampling image log of surface portions of the sample. Repeat the above steps in order to construct three dimensional (3D) sample images from stacked successive pattern based simulations so as to construct at least one 3D model of the sample.

Abstract: Methods for upscaling digital rock modeling data are described. Core-plug samples for pore-scale modeling are strategically chosen using whole-core minipermeability grids and conventional CT (Computed Tomography) scans. Pore models or pore-network models are used for flow modeling. Computed numerical SCAL (Special Core AnaLysis) properties are validated using laboratory-derived data, then they are used to populate borehole-scale models. Borehole-scale models use MPS (Multi-Point Statistics) to combine minipermeability grids and conventional CTscans of whole core with electrical borehole images to create 3D numerical pseudocores for each RRT (Reservoir Rock Type). SCAL properties determined from pore-scale models are distributed for each petrophysical facies in numerical pseudocores. Effective SCAL properties computed from various MPS borehole-scale realizations or models are used to populate interwell-scale models for each RRT.

Abstract: Methods for characterizing a sample of porous media using a measuring device along with a multipoint statistical (MPS) model. Retrieving a set of reflected measured data provided by the measuring device of a surface of the sample in order to produce a sample imaging log, wherein the retrieved set of measured data is communicated to a processor. Selecting depth-defined surface portions of the sample from the sample imaging log as a training image for inputting in the MPS model. Determining pattern based simulations from the training image using one of a pixel-based template which is applied to the training image. Constructing from the pattern based simulations a complete-sampling image log of surface portions of the sample. Repeat the above steps in order to construct three dimensional (3D) sample images from stacked successive pattern based simulations so as to construct at least one 3D model of the sample.

Abstract: Methods for characterizing a three-dimensional (3D) sample of porous media using at least one measuring tool that retrieves two or more set of transmitted measured data at two or more depths of the sample, such that the retrieved two or more set of transmitted measured data is communicated to a processor and computed in at least one multi-point statistical (MPS) model.

Abstract: Methods for characterizing a geological formation, the methods include retrieving measured data provided by a measuring tool along one or more logged borehole length for a borehole, another borehole or both in order to produce a borehole imaging log. Selecting depth-defined intervals of the borehole imaging log as training images for inputting in a multi-point geostatistical model. Determining pattern based simulations for each training image using a pixel-based template of the multi-point geostatistical model so as to obtain training image patterns. Using the pattern based simulation of each training image to assign to each of the training image a corresponding training image pattern. Constructing from the training image patterns one or more fullbore image log of a borehole wall of the borehole. Repeat the second to fourth steps through the one or more logged borehole length in order to construct fullbore images from successive, adjacent training images.

Abstract: 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.