Classifications

• • E—FIXED CONSTRUCTIONS
  • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
  • E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
  • E02D1/00—Investigation of foundation soil in situ
  • E02D1/02—Investigation of foundation soil in situ before construction work
  • E02D1/04—Sampling of soil
• • E—FIXED CONSTRUCTIONS
  • E21—EARTH OR ROCK DRILLING; MINING
  • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
  • E21B25/00—Apparatus for obtaining or removing undisturbed cores, e.g. core barrels or core extractors
• • E—FIXED CONSTRUCTIONS
  • E21—EARTH OR ROCK DRILLING; MINING
  • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
  • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells

Definitions

• Rock samples or "cores” may be collected from wellbores during the drilling process.
• Various analyses may be conducted using these cores, and these analyses may provide insight into the subterranean formation properties.
• photographs of the cores may be compared to measurements made downhole, e.g., using wireline and/or logging-while- drilling logs.
• the photographs may include multiple sections and/or depths of rock samples placed side-by-side in a box in columns, with the columns separated by dividers.
• the photographs of the core boxes (e.g., "core -box images”) may be digital representations, which may be loaded into analysis software. Prior to such loading, the rock sample portions of the core-box images may be separated from the less useful box divider portions thereof. Further, the multi-column core-box image may be re-arranged, so as to align the columns into one long column, generally according to depth. In other words, the rock sample data from the columns may be extracted, and the multiple columns may be placed on top of one another, so they may be viewed beside the log measurements.
• This process of extracting and rearranging is generally conducted manually, with a user employing an image editing program to selectively, one-by-one, cut the desired portion of the image from the composite photo and create a new image.
• an image editing program to selectively, one-by-one, cut the desired portion of the image from the composite photo and create a new image.
• core-box images there may be hundreds of core-box images, and thus this may be a lengthy and costly process.
• Embodiments of the disclosure may provide a method for processing a core-box image.
• the method includes obtaining a core-box image including a first column including a representation of rock samples that were collected from a first subterranean depth interval, a second column including a representation of rock samples that were collected from a second subterranean depth interval, and a divider between the first and second columns.
• the method also includes identifying, by operation of a processor, the first column, the second column, the divider, or a combination thereof, based on a color contrast in the core-box image, and removing, by operation of the processor, the divider from the core -box image.
• the method further includes displaying, e.g., using a display device, a representation of the first column and the second column in a well log, after removing the divider from the core-box image.
• the method also includes arranging the first column and the second column into a single column based on the first and second subterranean depth intervals, prior to displaying the representation of the first column and the second column.
• identifying the first column, the second column, the divider, or a combination thereof includes defining a window at a first location in the core-box image, determining a color contrast value of a portion of the core-box image that is in the window, determining that the window includes the divider when the color contrast value is above a threshold contrast value, and determining that the window is within one of the first and second columns when the color contrast value is below the threshold contrast value.
• the method also includes applying a quality-control measure to the core-box image after identifying the first column, the second column, or both.
• applying the quality-control measure includes determining a statistic representing a confidence of the identification of at least one of the first column, the second column, or the divider, determining that the statistic is below a threshold confidence value, and in response to determining that the statistic is below the threshold confidence value, adjusting a size of the window, the threshold contrast value, or a distance between the first and second locations that the window is moved.
• the statistic represents a difference between a first color contrast value in the window when the window is determined not to include the divider, and a second color contrast value in the window when the window is determined to include the divider.
• moving the window to a second location in the core -box image moving the window to a second location in the core -box image.
• Embodiments of the disclosure may also provide a computing system that includes one or more processors, and a memory system comprising one or more non-transitory computer- readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations.
• the operations include obtaining a core -box image including a first column including a representation of rock samples that were collected from a first subterranean depth interval, a second column including a representation of rock samples that were collected from a second subterranean depth interval, and a divider between the first and second columns.
• the operations also include identifying the first column, the second column, the divider, or a combination thereof, based on a color contrast in the core- box image, and removing the divider from the core -box image.
• Embodiments of the disclosure may further provide a non-transitory, computer- readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations.
• the operations include obtaining a core-box image including a first column including a representation of rock samples that were collected from a first subterranean depth interval, a second column including a representation of rock samples that were collected from a second subterranean depth interval, and a divider between the first and second columns.
• the operations also include identifying the first column, the second column, the divider, or a combination thereof, based on a color contrast in the core-box image, and removing the divider from the core-box image.
• Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.
• Figure 2 illustrates a flowchart of a method for processing a core -box image, according to an embodiment.
• Figure 3 illustrates an example of a core-box image prior to processing, according to an embodiment.
• Figure 4 illustrates a flowchart of a process for identifying columns and/or dividers in the core-box image, according to an embodiment.
• Figure 5 illustrates a flowchart of a process for applying a quality-control measure, according to an embodiment.
• Figure 6 illustrates a core-box image after the dividers are removed therefrom, according to an embodiment.
• Figure 7 illustrates a schematic view of a computing system, according to an embodiment.
• first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object could be termed a second object, and, similarly, a second object could be termed a first object, without departing from the scope of the disclosure.
• FIG 1 illustrates an example of a system 100 that includes various management components 1 10 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.).
• the management components 1 10 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150.
• further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).
• the management components 1 10 include a seismic data component 112, an additional information component 114 (e.g., well/logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis/visualization component 142 and a workflow component 144.
• seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.
• the simulation component 120 may rely on entities 122.
• Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc.
• the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation.
• the entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114).
• An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.
• the simulation component 120 may operate in conjunction with a software framework such as an object-based framework.
• entities may include entities based on pre-defined classes to facilitate modeling and simulation.
• object-based framework is the MICROSOFT ® .NET ® framework (Redmond, Washington), which provides a set of extensible object classes.
• .NET ® framework an object class encapsulates a module of reusable code and associated data structures.
• Object classes can be used to instantiate object instances for use in by a program, script, etc.
• borehole classes may define objects for representing boreholes based on well data.
• the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Figure 1 , the analysis/visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.
• the simulation component 120 may include one or more features of a simulator such as the ECLIPSETM reservoir simulator (Schlumberger Limited, Houston Texas), the INTERSECTTM reservoir simulator (Schlumberger Limited, Houston Texas), etc.
• a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.).
• a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc.).
• the management components 110 may include features of a commercially available framework such as the PETREL ® seismic to simulation software framework (Schlumberger Limited, Houston, Texas).
• the PETREL ® framework provides components that allow for optimization of exploration and development operations.
• the PETREL ® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity.
• various professionals e.g., geophysicists, geologists, and reservoir engineers
• Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).
• various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment.
• a framework environment e.g., a commercially available framework environment marketed as the OCEAN ® framework environment (Schlumberger Limited, Houston, Texas) allows for integration of addons (or plug-ins) into a PETREL ® framework workflow.
• the OCEAN ® framework environment leverages .NET ® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user- friendly interfaces for efficient development.
• various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).
• API application programming interface
• Figure 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175.
• the framework 170 may include the commercially available OCEAN ® framework where the model simulation layer 180 is the commercially available PETREL ® model-centric software package that hosts OCEAN ® framework applications.
• the PETREL ® software may be considered a data-driven application.
• the PETREL ® software can include a framework for model building and visualization.
• a framework may include features for implementing one or more mesh generation techniques.
• a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc.
• Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.
• the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188.
• Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.
• the domain objects 182 can include entity objects, property objects and optionally other objects.
• Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc.
• property objects may be used to provide property values as well as data versions and display parameters.
• an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).
• data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks.
• the model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.
• the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc.
• the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc.
• equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155.
• Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc.
• Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry.
• Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc.
• one or more satellites may be provided for purposes of communications, data acquisition, etc.
• Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).
• Figure 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159.
• equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159.
• a well in a shale formation may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures.
• a well may be drilled for a reservoir that is laterally extensive.
• lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.).
• the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.
• a workflow may be a process that includes a number of worksteps.
• a workstep may operate on data, for example, to create new data, to update existing data, etc.
• a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms.
• a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc.
• a workflow may be a workflow implementable in the PETREL ® software, for example, that operates on seismic data, seismic attribute(s), etc.
• a workflow may be a process implementable in the OCEAN ® framework.
• a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).
• FIG. 2 illustrates a flowchart of a method 200 for processing core-box images, according to an embodiment.
• the method 200 may include obtaining a plurality of core-box images, as at 202.
• the core-box images may contain rock samples collected while drilling, or otherwise collected from, a wellbore.
• the method 200 may thus operate on one or more of the core-box images at a time, e.g., in sequence or in parallel, as discussed below.
• Figure 3 illustrates an example of a core-box image 300, according to an embodiment.
• the core-box image 300 may include several columns, e.g., six columns 302(1), 302(2), 302(3), 302(4), 302(5), and 302(6), which may be at least partially filled with representations of rock samples.
• the columns 302(l)-(6) may be separated by dividers, e.g. five dividers 304(1), 304(2), 304(3), 304(4), and 304(5).
• the dividers 304(l)-(5) may also be considered to include an outer frame 305 of the box.
• the dividers 304(l)-(5) may thus form part of the box, rather than part of the rock samples, and thus it may be desired to avoid considering the dividers 304(l)-(5) as part of the columns 302(l)-(6), e.g., when performing an analysis of the core.
• the columns 302(l)-(6) may be arranged according to the depth beneath the Earth's surface from which the rock samples were taken. Each column 302(l)-(6) may thus represent a depth interval, which may, in some embodiments, be at least roughly uniform as between columns 302(l)-(6).
• the core-box image 300 may likewise represent a depth interval, e.g., equal to the number of columns 302(l)-(6) multiplied by the depth interval for each. Dozens, hundreds, or more of such core-box images may be obtained from a single core sample, e.g., by placing the rock samples in the box according to depth and acquiring an image (e.g., taking a digital photograph) thereof.
• the digital filenames created from the acquired images may be referenced according to depth.
• the method 200 may also include identifying, by operation of a processor, the columns 302(l)-(6), as at 206 and/or identifying the dividers 304(l)-(5), as at 208.
• the columns 302(l)-(6) may be irregular in shape, e.g., curved or otherwise departing from straight, and thus to automatically delineate the columns 302(l)-(6), a fuzzy logic technique may be employed.
• Figure 4 illustrates a process 400 for identifying the columns 302(l)-(6) and/or the dividers 304(l)-(5), e.g., employing such fuzzy logic, according to an embodiment.
• the process 400 may include defining an analysis window 208 in the core-box image 300, as at 402.
• the window 308 is illustrated in Figure 3 as a square, it will be appreciated that the window 308 may be a circle, another polygon, or any other shape.
• the process 400 may then determine a color contrast within the window 308, as at 404.
• the process 400 may include determining a maximum, average, or another statistic related to the range of color values of the various pixels or other subareas of the window 308, in order to determine a value for the the color contrast.
• the process 400 may also include determining whether the color contrast is above a threshold contrast value, as at 406. Since the rock samples in the columns 302(1 )-(6) may be taken from a similar subterranean location, adjacent rocks in a column 302(l)-(6) may be expected to have a generally similar color, yielding a relatively low-contrast. Further, the color of the rock samples may be distinct from the color of the dividers 304(l)-(5), yielding a relatively high contrast if a window 308 includes both a divider 304(l)-(5) and a part of one of the columns 302(l)-(6).
• the window 308 may be determined to be within a column 302(l)-(6), as at 408, and if the contrast is high, the window 308 may be determined to include a divider 304(l)-(5), as at 410.
• the process 400 may then determine whether to analyze another window 308 in the image 300. That is, the process 400 may have defined the window at 402 in a first location in the core-box image 300, and may determine whether to move the window 308 to a second location in the core box image 300. If another window 308 is available (e.g., the full image 300 has not yet been analyzed), the process 400 may proceed to moving the window 308, as at 414, e.g., to analyze the color contrast of another area, which may or may not be overlapping with the previous area. In this way, the process 400 may loop back to 404 and iterate through, e.g., multiple times as the window 308 is moved through all or a subarea of the core -box image 300.
• the process 400 may include determining the location, size, shape, and/or any other characteristic of the columns 302(l)-(6) and/or the dividers 304(1 )-(5), as at 416.
• This information may be, for example, stored in a file.
• the shape and/or size of the columns 302(l)-(6) and/or dividers 304(l)-(5) determined may be used in quality-control measures, e.g., to compare different core-box images 300 and/or columns 302(l)-(6) and/or dividers 304(l)-(5) within a single core -box image 300, as will be described in greater detail below.
• the method 200 may proceed to removing the dividers 304(l)-(5) from the core-box image 300, as at 210.
• the method 200 may also, in some embodiments, include arranging the columns 302(l)-(6) into a single column, e.g., according to depth at which the samples were acquired.
• the method 200 may also include applying a quality-control measure, which may be employed on a batch or continuing basis.
• the quality-control measure may be implemented as at 212, e.g., after one or several core-box images 300 have been analyzed.
• the method 200 may employ the quality-control measure after removing the dividers 304(l)-(5); however, in other embodiments, the method 200 may apply the quality-control measure at 212 before or during removing the dividers 304(1 )-(5) at 210.
• This quality-control measure may be or include a "self-study" process, whereby the processor executing the method 200, in some cases, with input from a human user, may determine whether the column/divider identification is performing accurately and adjust if needed.
• FIG. 5 illustrates an example of a process 500 for applying such a quality-control measure at 212 in Figure 2, according to an embodiment.
• the process 500 may include determining a confidence in the color contrast determination, as at 502.
• the confidence may be represented by a statistic representing a difference between the color contrast of a window 308 deemed to not include a divider 304(l)-(5) and a window 308 that is deemed to include a divider 304(l)-(5).
• this statistic may simply be a comparison of the highest contrast for a window 308 that is deemed to not include a divider 304(l)-(5) and the lowest contrast of a window 308 that is deemed to include a divider 304(l)-(5).
• Other statistics, employing means, medians, or more complex sampling, may be employed, without limitation.
• determining the confidence at 502 may include comparing the size, shape, color contrast, or any other determined characteristic(s) of the columns 302(l)-(6) and/or dividers 304(1 )-(5) within a single core-box image 300 and/or across several core-box images 300.
• a uniformity of the sizes and shapes of the columns 302(l)-(6) and/or dividers 304(l)-(5) may be analyzed, with it being expected that a relatively high uniformity in the shapes may represent a higher confidence level.
• the process 500 may then compare the confidence to a threshold confidence level, as at 504.
• the threshold confidence level may be pre-programmed, user-defined, or in any other way defined. If the confidence is below the threshold, the process 500 may proceed to adjusting one or more parameters of the process 400 ( Figure 4) for determining the location of the dividers 304(l)-(5) and/or the columns 302(l)-(6), as at 506. For example, the process 500 may cause a greater number of analyses to be conducted in the process 400. This may be done by reducing the size of the window 308 and/or reducing the difference in area between two successive windows 308 (e.g., make them overlap more).
• the color contrast may be determined over a smaller area or a smaller differential area as between windows 308. Further, the threshold color contrast value applied at 406 ( Figure 4) may be adjusted. The process 500 may then cause the process 400 to run one or more additional times, but with the adjusted parameter(s), either re- analyzing previously considered core -box images 300 or applying the changed parameters going forward for additional core-box images 300.
• the method 200 may include determining whether one or more additional core -box images 300 are to be analyzed, as at 210. If there are additional core-box images 300 to analyze, the method 200 may loop back to the process 400 of identifying the columns and/or dividers after loading a new core -box image 300. Otherwise, the method 200 may be complete. It will be appreciated that the quality-control measure at 208 may be employed after each, some, or a certain number of core-box images 300 are analyzed and/or at certain time intervals, upon user instructions, etc.
• any one or more aspects of the method 200 and/or processes 400, 500 may be suited for, and accomplished using, parallel processing techniques.
• each color contrast determination in each window 308 may be independent, and thus multiple windows 308 may be analyzed in parallel.
• color contrasts for multiple different core-box images 300 may be analyzed in parallel.
• FIG. 6 A result of at least a portion of the method 200 may be illustrated in Figure 6, according to an embodiment.
• the core-box image 300 of Figure 6 has had the dividers 304(l)-(4) (and the frame 305) removed, leaving the rock samples in the columns 306(l)-(6).
• These columns 306(l)-(6) may then be rearranged, if desired, e.g., stacked top-to-bottom based on depth, and a representation thereof may then be provided along side of or in a view of a well log, as at 216 ( Figure 2).
• the rock samples, or an attribute thereof may be shown next to a well log of measurements of another parameter (e.g., resistivity) according to depth, such that the depth from which the rock samples are acquired matches the depth of the measurements.
• a display of a physical object may be altered in such a way to enhance the operation of the computer and to advance the technological field of borehole analysis.
• the methods of the present disclosure may be executed by a computing system.
• Figure 7 illustrates an example of such a computing system 700, in accordance with some embodiments.
• the computing system 700 may include a computer or computer system 701 A, which may be an individual computer system 701 A or an arrangement of distributed computer systems.
• the computer system 701 A includes one or more analysis modules 702 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 702 executes independently, or in coordination with, one or more processors 704, which is (or are) connected to one or more storage media 706.
• the processor(s) 704 is (or are) also connected to a network interface 707 to allow the computer system 701 A to communicate over a data network 709 with one or more additional computer systems and/or computing systems, such as 70 IB, 701C, and/or 70 ID (note that computer systems 70 IB, 701C and/or 70 ID may or may not share the same architecture as computer system 701 A, and may be located in different physical locations, e.g., computer systems 701A and 701B may be located in a processing facility, while in communication with one or more computer systems such as 701C and/or 70 ID that are located in one or more data centers, and/or located in varying countries on different continents).
• 70 IB, 701C, and/or 70 ID may or may not share the same architecture as computer system 701 A, and may be located in different physical locations, e.g., computer systems 701A and 701B may be located in a processing facility, while in communication with one or more computer systems such as 701C and/or 70
• a processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.
• the storage media 706 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 4 storage media 706 is depicted as within computer system 701 A, in some embodiments, storage media 706 may be distributed within and/or across multiple internal and/or external enclosures of computing system 701 A and/or additional computing systems.
• Storage media 706 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY ® disks, or other types of optical storage, or other types of storage devices.
• semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories
• magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape
• optical media such as compact disks (CDs) or digital video disks (DVDs)
• DVDs digital video disks
• Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture).
• An article or article of manufacture may refer to any manufactured single component or multiple components.
• the storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.
• computing system 700 contains one or more image processing module(s) 708.
• computer system 701 A includes the image processing module 708.
• a single image processing module may be used to perform some or all aspects of one or more embodiments of the methods disclosed herein.
• a plurality of image processing modules may be used to perform some or all aspects of methods herein.
• computing system 700 is only one example of a computing system, and that computing system 700 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 4, and/or computing system 700 may have a different configuration or arrangement of the components depicted in Figure 7.
• the various components shown in Figure 4 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.
• the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices.
• information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices.
• geologic interpretations, models, and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein.
• This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 700, Figure 7), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

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