Borehole Image Analysis
AutoDip™ and TrendSetter™ ServicesAutoDip™ and TrendSetter™ services automate dip and dip trend analysis of EMI™, XRMI™, and OMRI™ borehole data. These services save time and provide high-quality data that can help spot “hidden features” in sedimentary beds and laminates.
AutoDip service automates high-resolution dip detection—a vast improvement on tedious manual dip picking. Unlike traditional dip computation methods, AutoDip service does not simply correlate raw resistivity data. This method operates independently of often inappropriate correlation parameters, such as correlation length, step length, and search angle.
TrendSetter service augments AutoDip functionality by taking dip data and automatically sorting it into categories: • Constant dip with depth
• Increasing dip with depth • Decreasing dip with depth
TrendSetter service helps characterize geologic features based on dip trends. AutoDip and Trendsetter services provide a continuous plot with a break out of dip trends and constant dips. These dips and trends can be easily recognized and incorporated into a geological model.
AutoDip and TrendSetter services differentiate themselves by selecting bedding features more quickly and consistently than hand picking. This provides more time to view the results and interpret the data.
Slumping and soft sediment deformation are evident in this section of log. The AutoDip™ program does a good job of capturing the changing dips.
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AutoDip™ Service
AutoDip™ service uses data from all resistivity buttons—not just 4, 6, or 8—to more accurately determine dips. By using more data, more accurate dip readings are possible.
AutoDip service translates the human visual experience of event correlation into an equation that quantifies visual recognition to obtain the optimal dip. The self optimizing algorithmic process¹ operates without the need to adjust correlation parameters, which can introduce bias into dips or even hide dips when using traditional methods.
The AutoDip program works equally well in simple bedding or in more complex bedding environments.
Applications
• High-resolution dip detection of EMI™, XRMI™, and OMRI™ borehole data to help spot “hidden features” in sedimentary beds and laminates
Features
• Uses all buttons to compute dips
• Uses quality curves to optimize dip selection • Removes user bias in selecting dips
• Consistent picks independent of interpreter bias • Output curves that indicate degree of laminations • Output curves that indicate degree of bed contrast • Independence from search angle, correlation length, and
step length
¹Shin-Ju Ye, et al., Automatic High Resolution Sedimentary Dip Detection on Borehole Imagery (SPWLA 38th Annual Logging Symposium, 1997)
TrendSetter™ Service
The AutoDip program can generate many dips. The number of dips is partially determined by dip quality filters. During the analysis process, it is prudent to look for patterns to help recognize trends that can impact mapping, offset wells, and describe depositional environments and structural changes. TrendSetter™ service automatically separates dips into constant, increasing, and decreasing categories, making it easier to visualize changes and trends.
TrendSetter service separates the dips from stratigraphic events such as current bedding, slumps, and drapes from the more constant structural dips, which allows better estimates of local structural dip.
Applications
• Dip trend analysis of EMI, XRMI, and OMRI borehole data to help spot “hidden features” in sedimentary beds and laminates
Features
• Automates the selection of dip trends
• Provides quality curves used to control grade of trend • Removes scatter from structural dip trend
• Identification of other stratigraphic or structural events when used with other geologic data
• A user interface that provides flexibility and quality control
Inputs EMI™, XRMI™, and OMRI™ data set
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Trendsetter™ service eliminates need for hand-selecting dip trends.
ReadyView™ Open-Hole Imaging System
The ReadyView™ system provides easy-to-use interactive software for the analysis of acoustic and electrical wellbore image data. The ReadyView™ system consists of three separate applications that provide image and dip interpretation and 3D visualization of the wellbore. The ReadyView™ system can be used to determine both true and apparent bedding dip and can also be used to determine the distribution, orientation, and apparent aperture of natural and drilling-induced fractures.
This innovative system uses a customized USB flash drive to store and launch the applications. Performance on the flash drive is comparable to running on a local hard drive. It provides unique accessibility to all types of wellbore image data, along with measurement and classification tools required for borehole breakout, structural, stratigraphic, and formation evaluation applications of image analysis.
The software can be modified or augmented to meet the specific requirements of individual clients.
Features
• Runs on Microsoft® Windows® 2000 and Microsoft Windows XP platforms
• Works with a wide range of wellbore image data including third party imaging tools, allowing full 32-bit RGB color resolution of acoustic and electrical image data
• User interface provides a comprehensive set of sophisticated, interactive measurement tools and the ability to more easily classify and describe features observed in these logs. In particular, the ReadyView system offers a series of 2D data filters (i.e. Sobel, Gaussian, Sharpen, Horizontal edge detection) to enhance the image
• Planar features can be displayed in stereographic projections or rose diagrams; tadpole profiles can be used to display planar data or wellbore trajectory • Standard log profiles, including gamma and resistivity
logs, can be imported and displayed as reference logs for formation interpretation
• Menus and dialog boxes allow quick scrolling, resizing, and selection of intervals of the data, making image analysis easy and straightforward
• Customers can pick and interpret their own dips, save and restore their own dip results, and export them in a variety of formats for transfer to other systems including OpenWorks™ and Microsoft Excel® applications • All image projections and data analysis views may be
saved in a variety of raster and vector formats for report generation
• Allows wellbore image data to be easily viewed in full color unwrapped views, polar cross-sections, 3D cylindrical displays, log profile views, and many others • The ReadyView system is also an excellent archival
system for use of the digital image data at a future date
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This example shows a 2D view of image data when launching ReadyView™ software. From left to right, the first tract is a compressed image, second tract static image, third tract is depth, fourth tract is a dynamically enhanced image and the right side is a Schmidt plot, image histogram, and a dip-azimuth plot.
Facies Profile
Facies profile is a multi-dimensional, dot-pattern
recognition, clustering method based on nonparametric K— nearest neighbor and graph data representation. The underlying structure of the data is analyzed, and natural data groups are formed that may have very different densities, sizes, shapes, and relative separations. Facies profile
automatically determines the optimal number of clusters, yet allows the analyst to control the level of detail actually needed to define the electro-facies.
Facies profile partitions the reservoir into discrete electro- facies or flow units. Producing electro-facies is a common and valuable operation performed by oil companies to discriminate discrete reservoir components. These components are used to populate reservoir models, flow simulators, determine porosity/permeability relationships, and describe the reservoir.
The facies profile model can be run with conventional log data (such as GR, RHOB, or ΔT), NMR data, and possibly other data (not yet tested). A texture profile model based on the same clustering method has been developed to extract texture from electric (EMI™, XRMI™, and OMRI™ data) image data.
The facies profile analysis shows similarities to a core description that might be done on a whole core or outcrop. Grain size, cleanliness, or porosity increase toward the right and changes in facies correspond to different colors and patterns. The Facies profile analysis contains automatic ordering that performs the grain size or porosity function automatically. The lowest numbered electro-facies has the smallest grain size or porosity and the highest number electro-facies has the largest grain size or porosity. The smaller numbered facies would plot farthest to the left, and the larger numbered facies would plot farthest to the right.
Applications
• Log interpretation that helps define 3D reservoir facies models describing the distribution of porosity,
permeability, and capillary pressure in more detail than is possible with reflection seismology
• Determination of the optimal number of clusters, while still allowing the analyst to control the level of detail actually needed to define the electro-facies
Track 4 shows nine electro-facies computed from the GR (Track 1 black) RHOB (Track 3 red) NPHI (Track 3 green) and PE (Track 3 magenta). Using these four inputs, facies profile is able to discriminate differences in the lithology (but not actual lithology) and automatically order them according to increasing grain size or porosity. The EMI™ image in Track 1 is provided to show the correlation between the image and electro-facies.
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Features
• Helps define layering and select the best options for production test interpretation
• Integrates geological insight into conventional log analysis
• Automatically clusters and orders log data for generating electro-facies. It processes conventional log data, array data, such as NMR T2 distribution, image texture parameters, (texture profile), or any combination of a wide range of data
• Partitions the natural pattern of the data without requiring the user to give the number of clusters
• Automatically proposes optimal number of clusters. Clusters are organized in a hierarchical way which can ease the interpretation
• Automatically orders the clusters in log space which uses coarse-to-fine self-organizing map (CFSOM). This ordering usually corresponds to the geological facies evolution order which is particularly important for assessing geological meaning of each of the facies and their vertical sedimentary sequences
Inputs
All the input curves must have the same step. Halliburton recommends placing the input curves to be used in a separate CLS file because numerous new curves may be generated. The output will have the step of the CLS file.
ALPHA–The higher ALPHA the greater the smoothing. ALPHA can vary from 1 to 500. This parameter has been optimized and it is highly recommended that the user leave it at the default of 10.
K–Another smoothing parameter that can vary from 4 to 20. The higher the number the greater the smoothing. K has also been optimized and it is recommended that the user leave it at the default of 5. The minimum number of electro-facies to compute. The maximum number of electro-facies to compute. The number of optimal electro-facies models generated by the program.
Outputs
EFAC_1, EFAC_2, and EFAC_3EFAC—stands for electro-facies. EFAC_1, EFAC_2, etc. are generated electro-facies model 1, 2, etc. Also see PARAMETER OM.
Gives cluster kernels in log space order (after automatic ordering). The Kernel Representative Index of each data point.
The Neighboring Index of each data point. It is unique for each data point for a given ALPHA. It measures the local data density around each point. Higher its value, higher its local density.
The normalized Neighboring Index of each data point within the cluster. Because the cluster member configuration change with different electrofacies models, NNI is different with different electro-facies models.