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4 Oilfield Review

Imaging: Getting the Picture Downhole

Geologists and petrophysicists use image logs to visualize rocks in situ and determine

structural geometry and formation properties. Image data help them analyze reservoir

properties such as heterogeneity, sedimentary conditions and structural features,

including fractures, folds and faults. Engineers have found that acquiring images

in oil-base mud systems is difficult because the insulating properties of oil often

renders conductivity-based imaging tools ineffective, especially for fracture analysis.

That limitation has been addressed with a newly introduced imaging tool for

oil-base mud systems.

Janice BrownFort Worth, Texas, USA

Bob DavisOklahoma City, Oklahoma, USA

Kiran Gawankar Southwestern EnergyThe Woodlands, Texas

Anish KumarBingjian LiCamron K. Miller Houston, Texas

Robert LarongaPeter SchlichtClamart, France

Oilfield Review 27, no. 2 (September 2015).Copyright © 2015 Schlumberger.adnVISION, FMI, FMI-HD, Formation MicroScanner, MicroScope HD, OBMI, OBMI2, Quanta Geo, Sonic Scanner, SonicScope and UBI are marks of Schlumberger.

A picture is worth a thousand words because visualizing an object or concept is a powerful means of assimilating large amounts of informa-tion. Geologists and petrophysicists may use imaging tools to visualize downhole formations. These tools provide information that can be cru-cial for determining rock and formation proper-ties, especially when physical core samples are not available. Wireline logging tools that can image the borehole are based on dipmeter tools, which were originally designed to determine for-mation geometry and structural properties.

The evolution of imaging tools is part of a long history of petrophysical tool development. The first wireline logs were euphemistically referred to as electrical coring; some of the early logging units displayed “Electrical Coring” below the Schlumberger name (Figure 1). And yet, early wireline logs offered far too little information to substitute for coring. Service providers advancing the science of well logging have developed tools that probe the structure and mineralogy of for-mations almost to the level available from studies performed on cores.1 Images that represent the

Figure 1. Electrical coring. As evidenced by this 1932 photograph from the California, USA, oil fields, the originators of the wireline logging industry envisioned the concept of electrical coring.

Oilfield Review SEPTEMBER 15Imaging Fig 1ORSEPT 15 IMG 1

1. For more on coring services: Andersen MA, Duncan B and McLin R: “Core Truth in Formation Evaluation,” Oilfield Review 25, no. 2 (Summer 2013): 16–25.

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electrical properties of the borehole may also provide geologists with core-like visualizations of downhole conditions.

Cores, however, are preferred by geologists studying downhole rock properties although the length of coring intervals is often limited by costs, and physical changes in the cores can occur while bringing the samples to the surface. From a cost and coverage standpoint, modern logging tools can sometimes provide details of

the reservoir that might otherwise be unavailable from physical cores. Although images cannot replace cores, they can provide qualitative and quantitative visual information when core is absent; from a visual perspective, they are per-haps the closest devices available for meeting that original electrical coring vision.

The first imaging devices—introduced in the 1980s—were developed from tools designed to acquire dipmeter measurements.2 Dipmeter tools

use a combination of electrical and mechanical sensors to acquire data from which the magni-tude and direction of formation dip can be deter-mined. Geologists use dip information to help them understand the subsurface geometry of geo-logic structures; the information may then be used to project structural geometry away from the borehole out into the formation.

Continuous improvements and changes in hardware, measurement physics, processing power,

Figure 2. LWD azimuthal imaging. The bulk density image from an adnVISION tool (Track 3) provides information about the borehole circumference in a horizontal well. Density data are also presented as curves (Tracks 3, 4 and 5) and are displayed according to the quadrant from which the data were acquired (Tracks 3 and 5). Bulk density and neutron porosity data may be

affected by hole conditions as can be observed around X50 ft, where the caliper indicates a washout (Track 1, blue shading). Because this well is horizontal, the tool’s azimuthal outputs are referenced to up, down, left and right. In a vertical well, the references are north, south, east and west.


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data transmission and tool technologies eventually led to imaging tools that provided much more than formation dip. Imaging tools acquire high-resolu-tion conductivity (or the reciprocal resistivity) data from a very shallow depth of investigation and provide an image of a portion of the wellbore cir-cumference. These images are representative of features intersecting the borehole.

By interpreting information generated by both computer processing and manual correlations, geologists can identify geologic features. Before imaging tools were available, geologists used dip-meter data primarily for guidance in planning the next well location. They were able to determine the direction needed to move up or down struc-ture, the location of faults and the presence of structural anomalies. Modern image logs provide an opportunity to better understand reservoir geo-logic characteristics and visualize the well within the context of the reservoir.

Advancing beyond dipmeter tools, imaging tools now allow interpreters to identify structural features such as faults, folds, angular unconformities and bed-ding geometry and infer paleotransport direction of sands and conditions that existed during deposition. Geologists can also use image logs to detect fractures and define their properties—a crucial element in characterizing tight reservoirs. They then incorpo-rate fracture properties in completion designs and use the information for field optimization.

The ability to detect small features such as fractures is not easily performed in wells drilled with oil-base mud (OBM) systems.3 The mud and mudcake add a layer of electrical insulation in the wellbore that usually renders traditional con-ductivity-based imaging tools ineffective. Imaging

tools designed for use in OBM systems have not delivered the level of resolution that tools designed for water-base mud (WBM) systems are able to provide—determining quantitative properties of fractures has been especially difficult. The Quanta Geo photorealistic reservoir geology service, which acquires images that are representative of the borehole wall in the challenging environment of OBM systems, was recently introduced to address this situation.

This article reviews the evolution of imaging ser-vices—from dipmeter tools to the latest generation imaging devices. Case studies demonstrate the use of image logs in OBM wells for stratigraphic analysis of wells drilled in deepwater Gulf of Mexico environ-ments and for analyzing fractures in wells drilled in unconventional reservoirs.

Painting a Wellbore PictureBefore computers became readily available, relatively high-resolution dipmeter data were acquired from downhole, and the information was presented on photographic film. Analysts read and interpreted these data manually—a tedious process. The introduction of computer-ized logging units and digital data processing enabled higher sample-rate data to be acquired than was previously possible. Modern logging tools acquire more information than most humans can assimilate, integrate and process. Computer processing has become indispensable for delivering information in a usable format.

The ability of logging-while-drilling (LWD) tools to make azimuthal measurements from around the circumference of the borehole has also changed the way many analysts visualize

downhole data. In a similar manner to that used by conventional wireline logging devices, LWD tools acquire data linearly via tool movement along the well; however, azimuthal tools also acquire data from the full circumference of the wellbore as the tool rotates. Azimuthal data are then presented as an image of the borehole, “painting a picture” of the inside of the wellbore. Because the tool orientation is measured simul-taneously, the images can be aligned with the geometry of the wellbore. However, the resolu-tion of these data is insufficient for detecting small details (Figure 2).

Many LWD tools can provide azimuthal data presented in the form of wellbore images; such tools include azimuthal gamma ray devices, the MicroScope HD high-definition imaging-while-drilling tool and the adnVISION azimuthal den-sity neutron service.4 Image interpretation of data from azimuthal tools has become crucial for adjusting wellbore trajectory—up, down, left or right—in real time in many horizontal drilling operations (Figure 3).

Figure 3. Well placement using image data. Azimuthal log data in the shapes of smiles and frowns help well placement engineers determine bit corrections while drilling. When a wellbore crosses a bedding plane, the azimuthal logging tool response indicates whether the wellbore is exiting an ascending or descending geologic layer. When the wellbore cuts an ascending layer (left ), the first contact with the formation is at the bottom

of the hole; when the bit exits the layer, the last contact will be at the top of the hole. The image data appear as a frown. Conversely, measurements from a wellbore that exits a descending bedding plane (right ) appear as a smile. Based on these interpretations, drilling engineers may guide the bit up or down to ensure that the wellbore remains in or reconnects with a target zone.


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2. For more on legacy imaging tool and image interpretation: Wong SA, Startzman RA and Kuo T-B: “A New Approach to the Interpretation of Wellbore Images,” paper SPE 19579, presented at the 64th SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, October 8–11, 1989.

3. For more on early logging services used for imaging in OBM systems: Cheung P, Hayman A, Laronga R, Cook G, Flournoy G, Goetz P, Marshall M, Hansen S, Lamb M, Li B, Larsen M, Orgren M and Redden J: “A Clear Picture in Oil-Base Muds,” Oilfield Review 13, no. 4 (Winter 2001/2002): 2–27.

4. For more on LWD azimuthal imaging tools and using azimuthal data for structural steering: Amer A, Chinellato F, Collins S, Denichou J-M, Dubourg I, Griffiths R, Koepsell R, Lyngra S, Marza P, Murray D and Roberts I: “Structural Steering—A Path to Productivity,” Oilfield Review 25, no. 1 (Spring 2013): 14–31.

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Wireline logging tools were the first to acquire data that could be displayed as images from the circumference of a wellbore although few wireline tools have the azimuthal acquisition capabilities commonly found in LWD tools. An exception is the UBI ultrasonic borehole imager tool, which uses a rotating assembly to map the full circumference of the borehole from ultrasonic reflections of the borehole wall. Because the UBI tool depends on the quality of the reflections from the borehole, it works best in hard formations.

Older generation devices that have multiple pads, such as the HDT high-resolution dipmeter tool, acquired data from four regions inside the borehole. By correlating the data acquired from around the wellbore, bedding or feature dip mag-

nitude and direction could be determined manu-ally or by computer. Successive generations of dipmeter tools increased the number of sensors and pads, ultimately giving way to tool designs that had sufficient sensor density to provide imaging capabilities (Figure 4).

The FMS Formation MicroScanner tool was one of the first successful borehole imaging ser-vices. Equipped with four pads, the original tool had 27 sensors on two of the pads, which acquired data every 2.5 mm [0.1 in.].5 The other two pads had only two button sensors each. This design permitted basic imaging of the borehole; how-ever, covering the inside of the wellbore required multiple passes and manual depth matching. An updated FMS tool had two rows of eight sensor

buttons on each of its four pads, which covered more of the borehole in a single pass.

The FMI-HD high-definition formation micro-imager is the latest generation Schlumberger tool for assessing structure and stratigraphy of rocks in WBM systems and some OBM systems.6 This tool is equipped with 192 pad-mounted sen-sors, or button electrodes, and samples every 2.5 mm (Figure 5). The button electrodes are arranged in parallel rows across the face of each pad, and each pad has a hinged flap extension that has its own parallel rows of sensors. When the pads, which are mounted on caliper arms, are extended, the flaps open and increase the cir-cumferential coverage of the borehole. In an 8-in. borehole, the tool covers 80% of the circumfer-ence. The design results in a 5-mm [0.2-in.] reso-lution; any feature 5 mm or larger can be directly measured although much smaller features, including fractures, can be imaged if there is suf-ficient electrical contrast with the background.

For interpreters to visualize these data, the measurements are converted from conductivity values into images. These images are created from the electrical measurements, which are converted to pixels. Before image logs existed, however, dipmeter interpretation relied on tad-poles computed from wellbore data.

Answers in the TadpolesLog analysts still use tadpoles from dipmeter logs to describe downhole structural geometry and stratigraphy. Tadpoles represent information computed from raw dipmeter data; they provide two main quantities: dip direction and dip magni-tude (Figure 6). Each tadpole consists of a head and a tail. The head of the tadpole is plotted on a graph scaled from 0° to 90°, and the position of the head on the scale indicates the magnitude of the dip. The tail points in the downward direc-tion, or dip, of the formation or feature, and the display is based on a compass dial. True north is at the top followed clockwise by east, south, west and back to north through a full 360° cycle. By reading the dip magnitude from the location of the head and the direction from the tail, inter-preters infer formation or feature geometry.

Tadpoles are computed from data acquired as the tool traverses the borehole during logging; if bedding planes with contrasting resistivities are

Figure 4. Imaging evolution. The original dipmeter tool from 1945 had three pads; each pad had a single sensor button (top left ). As successive generations of tools were developed, engineers added pads and increased the number of sensor buttons on each pad. The FMS tool (bottom left ), introduced in the mid-1980s, was one of the first wireline tools to provide image logs. Developers found that multiple parallel rows of buttons in the original design were not necessary, and the original FMS tool was modified in 1988 to have only two rows of sensors on each of its four pads for a total of 64 sensors (bottom middle). The FMI fullbore formation microimager (bottom right ), introduced in 1991, has four pads that have four flaps and a total of 192 sensors. The wellbore schematic below each tool shows coverage by the pads in an 8-in. borehole.


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5. An FMS tool with four pads for imaging was a forerunner of the FMI-HD tool. For more: Bourke L, Delfiner P, Trouiller J-C, Fett T, Grace M, Luthi S, Serra O and Standen E: “Using Formation MicroScanner Images,” The Technical Review 37, no. 1 (January 1989): 16–40.

6. For more on the FMI service: Adams J, Bourke L and Buck S: “Integrating Formation MicroScanner Images and Cores with Case Studies,” Oilfield Review 2, no. 1 (January 1990): 52–65.

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encountered, the tool will detect those events along the borehole (Figure 7). Should all pads detect an event at the same depth, the relative dip is 0°. If the sensors encounter a dipping bed or feature crossing the wellbore, the sensors detect it at various points inside the borehole. The magnitude of dip is determined by comput-ing the displacement of these events. A structural dip of just 1° will cause approximately 5 mm of displacement across an 8-in. borehole, which is within the resolution range of the tool.

The position of one pad is referenced with respect to true north, which determines the ori-entation of the tool. This also defines the posi-tion of the other pads and sensors. The orientation of the pads in the borehole along with the displacement between conductive or

Figure 5. Latest generation imaging tool for WBM systems. The FMI-HD tool, which has four pads and four flaps, has a total of 192 button sensors. The caliper arms extend, and the flaps rotate to provide an acquisition surface that is twice as wide as that of tools that have only four pads. The close spacing and fixed distances between sensor buttons result in high-resolution data; fixed spacing provides a systematic method for speed correction. The tool generates a continuous stream of high-resolution data (inset ) from its 192 buttons from which images are generated.


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Figure 6. Tadpole plots from dipmeter data. A single tadpole computed from dipmeter tool data indicates a variety of reservoir geometric properties. The location of the head of the tadpole on the scale indicates the magnitude of formation dip. The tail of the tadpole points in the downward direction. This example tadpole indicates formation dip of 27° down to the west. Tadpoles have evolved over the years to include color coding, quality indicators and modifications that represent fractures or other features.


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Figure 7. Detecting bed boundaries and formation dip. As a dipmeter tool is pulled through the wellbore (left ), sensors on the pads intersect the bedding plane or feature at various points along the borehole wall. By correlating the points and determining the tool’s cardinal coordinates (middle left ), the bedding plane’s geometry can be computed. When the data from along the inner surface of the borehole are unwrapped (middle) and presented in 2D (middle right ), a dipping bedding plane will form a sinusoid, which gives an

indication of the direction and magnitude of the formation dip. Analysts use images from the inner surface of the borehole wall to visualize formation geometry and identify features such as fractures and unconformities. The down dip direction in the image appears to be to the west, although most image data are presented using apparent dip. Based on the tadpoles computed from these data (right ), which include rotation for wellbore and tool drift, the true dip is down to the south.


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resistive events are used to define the depth, direction and dip magnitude of a bedding plane or a feature. The direction and magnitude are then presented as an apparent dip, which is

related to the tool orientation. This apparent dip can also be corrected for the angle and incli-nation of the wellbore, also referred to as bore-hole drift (Figure 8). Sensors measure the

position of the tool with respect to true north and determine tool deviation from vertical. When the contribution of the tool and well posi-tion are rotating out, the true formation dip from horizontal can be displayed as a tadpole.

A single tadpole is not sufficient for determin-ing formation geometry. In the past, dipmeter interpretation, which is both an art and a sci-ence, was a process whereby analysts identified trends or patterns in the tadpoles from whence downhole structures could then be described. The three primary patterns are often referred to by the colors red, blue and green (Figure 9). A red pattern is increasing dip magnitude with depth, a blue pattern is decreasing dip magni-tude with depth and a green pattern is uniform, or unchanging, dip with depth. The azimuth of the dips should be constant or changing slowly across the section or feature. Patterns can result from a variety of features, but interpreters used the patterns primarily as guides for selecting the direction of offset well locations or defining depo-sitional direction. Dipmeter interpretations are often used to explain why a well encountered unexpected or missing formations sections, for instance, as a result of crossing a fault.

Geologists interpreting dipmeter data and images today have gone far beyond recognizing red, blue and green patterns. From images, they are able to interpret downhole structure and stratigraphy.

Evolution: Tadpoles to ImagesTraditional tadpole pattern recognition involved taking a 2D concept and constructing a 3D vision of the reservoir. This macro view of the downhole environment was used to describe formation geometry, but the view inside the wellbore can show the interpreter much more about rock and formation characteristics. This task is accom-plished using borehole image data.

The conversion of tool measurements to images is analogous to the processes used in mod-ern digital photography. One type of digital camera in use today is the charge-coupled device (CCD).7 The heart of the camera is a densely packed array of sensors. Incoming photons strike a portion of the sensor surface and are converted to electrons (Figure 10). An analog-to-digital converter accu-mulates the charge information from these elec-trons and transmits it for further processing and eventual storage. The more densely packed the sensors are on the array, the greater the number of pixels and the higher the resolution.

7. Charge-coupled device sensors were invented by Willard Boyle and George Smith at AT&T Bell Laboratories, New Jersey, USA, in 1969.

Figure 8. Correction for borehole drift and formation geometry. Apparent dip (AD) is the computed angle of the formation bedding plane or feature as it crosses the borehole. True dip (TD) is AD corrected for the geometry of the well and tool drift; these rotated data reflect deviation from horizontal. Some stratigraphic features such as the paleodepositional direction can be more easily seen in data that have the structural dip deleted (not shown) because the resulting data may be representative of conditions that existed at the time of deposition.


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Figure 9. Pattern recognition dipmeter interpretation. Green patterns (Track 2) represent the general structural dip of a formation and are usually more consistent in low-energy depositional environments such as shales, as indicated in the correlation curve (Track 1) than in high-energy deposition typical of sandstones. Abrupt changes in structural dip can occur when a well crosses an unconformity or a fault. Red patterns are increasing dip with depth and may be indicative of approaching faults, drape over structures and channels. Blue patterns are decreasing dip with depth and may indicate bedding, paleodepositional direction, deformation below faults and unconformities. The borehole geometry can also be represented by tadpoles (Track 3). This well is drifting about 2° from vertical toward the ENE.


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Unlike in film photography, in digital photog-raphy, there is no “physical” image—photo-graphs are reconstructed from digital data that represent light falling onto the sensors. Similar to the process in which digital cameras convert signal data to pixels and collect pixels into images, the high-resolution conductivity data from the sensor buttons of imaging tools are con-verted to pixels and then displayed together as an image (Figure 11). The image is not an actual picture but a representation of the changes in conductivity along the inside of the wellbore.

Imaging ProcessData acquired during logging have little resem-blance to the final image product. The buttons produce a continuous stream of parallel conduc-tivity measurements, which are transmitted uphole and recorded. The 192 buttons of the FMI-HD tool—each of which has a 5 mm diame-ter—acquire a measurement with each 2.5 mm of tool movement. The tool’s horizontal and verti-cal sensor spacing, along with high sampling fre-quency, enable the tool to measure features as small as 5 mm, but it can resolve much smaller

Figure 10. Creating digital images. A charge-coupled device (CCD) camera consists of an array of sensors. Light (photons) strikes the surface of the CCD (left ), and the sensors detect the photons and convert them to electrons. Electrons are measured and converted to a voltage. The analog voltage measurement is sent to a processor, where the measurement is converted to digital data for storage. A CCD sensor does not create an image as film cameras do; the image is recreated from stored data at each pixel location. This process is similar to the process used for conversion of conductivity (or resistivity) data to pixels for creating image logs.


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Figure 11. Data from the FMI-HD tool converted to an image log. The 192 buttons located on the FMI-HD tool’s four pads and four flaps generate a stream of conductivity data (left ). These data are processed, the values are assigned a scaled color, and an image is produced (right ). The geologist analyzing the images can modify the color scale and range to enhance features. The cardinal location of Pad 1 can be identified from the green curve at the far right.


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events. Measurements such as tool position, the Earth’s magnetic field properties, caliper data and low-resolution sensor information are sam-pled every 3.8 cm [1.5 in.].

Raw data must be reviewed for quality, and corrections are applied as necessary during pro-cessing. A crucial step in the QC process is speed correction, in which the objective is to position each measurement at the correct depth in the borehole relative to all the other measurements. Speed correction attempts to overcome nonuni-form tool movement and ensure data integrity. Even slight changes in tool movement during acquisition of high-resolution data can affect image quality.

Speed correction is often a two-step process. Accelerometers in imaging tools detect incremental tool movements; offsets for these small variations are applied as a first-level correction. Because the sensor buttons are arranged in parallel rows with a fixed spacing, changes in resistivity at boundary crossings can be compared. If the same event is found to be displaced between rows, the data can be shifted to adjust for the offset. Software-based tool movement detection methods help to further refine the initial speed correction. Combining the meth-ods produces a robust correction; however, when extreme tool movement irregularities occur, espe-cially those of the stick-and-release variety, data may not be recoverable.

The next step in processing is to harmonize the button responses (Figure 12). Raw button responses are not calibrated, but button-to-but-ton normalization can be used to ensure a reason-able image is generated. In this step, gains and offsets are computed for each button over a slid-ing window—typically 5 to 30 m [15 to 100 ft]—to give all of the buttons a comparable response. These normalized responses are then assigned a color or gray scale value and presented as an image of the borehole from 0° to 360°, with the left edge at 0° and the right edge at 360° representing true north. The center of the image at 180° repre-sents south. For horizontal wells, the top of the well is on the left and right (0° and 360°) edges and the bottom of the well is in the center of the image (at 180°).

Data are usually presented in both static and dynamically enhanced modes—the latter can increase the visible range of usable images (Figure 13). The static image helps the inter-preter maintain the image context—to recognize whether one is interpreting a conductive or resis-tive bed—and to recognize major bed boundaries by their association with significant resistivity changes. The dynamic image allows the inter-

Figure 12. Processing raw data. Streams of raw data from the button sensors (top) are depth shifted, offset and equalized (bottom). This processing produces more consistent data and better image quality than would be available from the raw data.


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Figure 13. From borehole conductivity to borehole images. After offsets and normalization are applied, the processed data are assigned a color or gray scale based on the measured conductivity (or resistivity). In this scheme, conductive features are represented by dark colors and resistive features are represented by light colors. Because the resistivity range of the tool is large, the data are usually presented in static mode and a dynamically adjusted mode. For static imaging, the peak value (green shading) corresponds to a color or gray shade. For dynamic scaling, the computer samples the data outside the peak value (blue shading) and uses the information to create an enhanced image. The color or gray scales may also be reversed to highlight resistive or conductive features. The various modes allow analysts to see details and features that might otherwise be masked.


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preter to see the maximum detail of formation texture and is useful for identifying sedimentary structures, textures associated with complex porosity systems and both natural and drilling-induced fractures. Static equalization alone may be used if the contrast range is small.

Images may be presented in a variety of colors or in gray scale. A typical color scheme, referred to as heated, uses a yellow-to-brown gradient that is scaled from light to dark extremes. The actual color is arbitrary but may help to highlight fea-tures. Comparing color image logs to physical cores can be disconcerting because the actual rock will not have as much physical contrast as the visual contrast typical of image logs. For that reason, some analysts prefer gray scale images for comparing images to core.

Computer interpretation software is often used first to analyze the data and generate tad-poles. The processed image data are then dis-played on a workstation, where image analysts, usually geologists, observe and identify features such as structural dip, faults, fractures, crossbed-ding planes and unconformities (Figure 14).

The process of image interpretation has been described as observation, interpretation and implication. The analyst’s first task is to review the data in search of recognizable or observable features. After features have been identified, the analyst interprets them by making manual picks. Because features striking the wellbore at an angle present themselves as sinusoids, the ana-lyst can define points along a feature and allow

the image workstation software to fit a sinusoid and compute formation dip or feature geometry. The software can also correct the data for bore-hole drift. Stratigraphic features may be more meaningful if the borehole dip is further cor-rected by subtracting the structural dip compo-nent, restoring the geometry to that of the apparent depositional orientation.

The final task of the image analyst is to assess the interpretation for implications. Analysis of the structural geometry may be used to help plan the next well, determine the lateral landing point or establish field development alterna-tives. Stratigraphic interpretation may include identifying depositional implications and apply-ing that information to understanding the nature of the rocks. Identification of both natural and induced fractures can be used in determining fracture properties, confirming in situ stress rela-tionships and designing effective stimulation and completion programs.

In addition to wireline imaging tools, other technologies are available for imaging boreholes. These include resistivity-based LWD logging tools and acoustic imaging tools run on wireline.

LWD ImagingAlthough many LWD tools provide image logs, the combination of accurate tool movement, high-resolution accelerometer data and high data transmission rates have given wireline imaging tools an advantage over LWD imaging services.

However, the inability of LWD images to resolve small features has been addressed by a novel processing approach developed by Schlumberger researchers.

The MicroScope HD service has 1-cm [0.4-in.] buttons and can sample every 5 mm. Although this tool design can provide high-resolution mea-surements, design alone is not sufficient to resolve small features because tool movement cannot be controlled to the level needed in the drilling environment. Complicating the depth control issue is the fact that LWD data are time based rather than depth based, and pipe move-ment at the drilling floor is indexed to tool move-ment downhole. The large separation between the point of acquisition and the depth reference affects resolution quality.

To overcome tool movement issues, high-reso-lution data are acquired with the MicroScope HD tool along with magnetometer-based tool orienta-tion data as the tool rotates.8 Since each tool has a fixed sensor spacing, data from the borehole circumference can be viewed as strips that have a constant and known thickness. The time-based measurements are converted to a depth-indexed image using high-resolution axial and azimuthal

8. For more on the LWD imaging technique: Allouche M, Chow S, Dubourg I, Ortenzi L and van Os R: “High-Resolution Images and Formation Evaluation in Slim Holes from a New Logging-While-Drilling Azimuthal Laterolog Device,” paper SPE 131513, presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, Barcelona, Spain, June 14–17, 2010.

Figure 14. A case for images. Structural dip can be identified in the raw data (left, Track 1). These data have been computer processed to generate tadpoles (Track 2). The red tadpoles are generated from data covering 1-ft intervals; the blue tadpoles are output from 2 ft of data. Computed results that have lower confidence are shown as open circle tadpoles; the lines represent the computed sinusoids. In the image display (center), the computer-generated tadpole is not related to a specific feature but indicates trends derived from the raw data. A fracture crossing the wellbore is more easily visualized from the image log than on the raw data,

and it has been marked by the analyst (right, purple) using image workstation software. The analyst traces the fracture and allows the software to compute its true dip magnitude and direction (purple tadpole). The fracture crosses the wellbore at an angle magnitude of around 87°; the true dip direction is down to the NE and its strike direction is NW–SE. The analyst can also trace features such as bedding planes (green lines and tadpoles) and faults and compare those with computer-generated results (blue and red). The formation dip is about 10°, dipping to the NNE, as indicated by both manual and computer-generated results.


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sensor positions. As the tool is advanced up or down the well, overlapping strips of data are acquired. These strips of data have features that are a fixed distance apart, which allows the image strips to be merged, correlated to axial tool movement and continually adjusted for depth (Figure 15).

Using image data from the MicroScope HD tool, analysts have been able to detect small fea-tures. Datasets from this service are quite large but are transmitted continuously during the drill-ing operation or retrieved from the tool when it

returns to the surface. As with many other imaging tools, this tool requires a conductive mud system.

Oil-Base Mud ImagingWell operators use OBM systems because they facilitate improved drilling performance.9 Since the 1990s, most deepwater wells have been drilled with OBM systems that use nonconductive fluids; such systems preclude the use of logging tools that function only in conductive fluids.10

Conductivity-based imaging tools rely on detecting small changes in conductivity along the

surface of a borehole wall. However, OBM and mudcake behave similar to electrical insulators, obstructing current flow. Therefore, acquiring wellbore images in OBM systems may not be fea-sible using tools designed for acquiring data in WBM; modifications to the FMI-HD tool, however, have enabled acquisition of images in some OBM environments.11

Early attempts to acquire dipmeter data in OBM wells were often met with frustration. Blades and scratchers were first used to remove mud and mudcake from the borehole wall to pro-

Figure 15. High-resolution LWD imaging. Tool movement for LWD tools is referenced to changes in drillpipe depth measured at the surface. Downhole LWD data are time based. Depth is derived by associating the time of acquisition to the depth measured at the surface. Depth accuracy available from this system of measurement is insufficient for resolving fine details and features. Schlumberger engineers developed a method that ties the fixed spacing of the tool sensors to the data and correlates depth to data.

Overlapping data, viewed as strips (top left ), are aligned and adjusted to match tool movement and then merged. Examples of noncorrelated and correlated data (top right ) demonstrate the enhanced image resolution. MicroScope HD image data can now be used to define structural features and fractures (bottom). A log analyst has marked the almost vertical resistive fractures (cyan) and faults (magenta and blue) crossing this horizontal wellbore along with the bed boundaries (green) cut by the wellbore.


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9. For more on drilling with OBM systems: Bloys B, Davis N, Smolen B, Bailey L, Houwen O, Reid P, Sherwood J, Fraser L and Hodder M: “Designing and Managing Drilling Fluid,” Oilfield Review 6, no. 2 (April 1994): 33–43.

10. Chen Y-H, Omeragic D, Habashy T, Bloemenkamp R, Zhang T, Cheung P and Laronga R: “Inversion-Based Workflow for Quantitative Interpretation of the New-Generation Oil-Based Mud Resistivity Imager,” Transactions of the SPWLA 55th Annual Logging Symposium, Abu Dhabi, UAE (May 18–22, 2014): paper LL.

11. For more on the FMI-HD service in OBM: Laronga R, Lozada GT, Perez FM, Cheung P, Hansen SM, Rosas AM: “A High-Definition Approach To Formation Imaging In Wells Drilled With Nonconductive Muds,” Transactions of the SPWLA 52nd Annual Logging Symposium, Colorado Springs, Colorado, USA, May 14–18, 2011, paper FFF.

12. Cheung P, Pittman D, Hayman A, Laronga R, Vessereau P, Ounadjela A, Desport O, Hansen S, Kear R, Lamb M, Borbas T and Wendt B: “Field Test Results of a New Oil-Base Mud Formation Imager Tool,” Transactions of the SPWLA 42nd Annual Logging Symposium, Houston (June 17–20, 2001): paper XX.

13. Bourke LT and Prosser DJ: “An Independent Comparison of Borehole Imaging Tools and Their Geological Interpretability,” Transactions of the SPWLA 51st Annual Logging Symposium, Perth, Western Australia, Australia (June 19–23, 2010): paper GGG.

14. Bloemenkamp R, Zhang T, Comparon L, Laronga R, Yang S, Marpaung S, Guinois EM, Valley G, Vessereau P, Shalaby E, Li B, Kumar A, Kear R and Yang Y: “Design and Field Testing of a New High-Definition Microresistivity Imaging Tool Engineered for Oil-Based Mud,” Transactions of the SPWLA 55th Annual Logging Symposium, Abu Dhabi, UAE (May 18–22, 2014): paper KK.

September 2015 15

vide an electrical path to ground, but these meth-ods did not prove feasible for imaging. The OBMI oil-base microimager tool was the first successful tool for imaging in OBM systems.12 This tool has four pads located 90° from each other; each pad has five pairs of sensors spaced 1 cm apart (Figure 16). This spacing provides approximately 1-cm vertical and horizontal resolution, and pad coverage is approximately 32% of an 8-in. well-bore. The OBMI tool delivers images of large fea-tures but is unable to detect fine details.

The OBMI2 integrated dual oil-base microim-agers features two stacked OBMI sondes oriented 45° apart. This design doubles the circumferen-tial borehole coverage. In general, the OBMI, OBMI2 and other OBM imaging devices do not image small features as well as their WBM coun-terparts do.

Not only can spatial resolution be a problem, the measurement technology used in most OBM imaging tools can introduce artifacts such as shadow beds on the shoulders of high-contrast environments, or the images may be affected by the orientation of the bedding planes. Mud-filled cracks and drilling-induced features often distort the image and mask formation geology. One inde-pendent study found that OBM imaging tools resolved an order of magnitude fewer sedimen-tary features compared with those resolved by tools run in WBM environments.13

Realizing the need for a high-resolution imag-ing solution, Schlumberger researchers began

developing a tool that could produce images in OBM systems comparable to those available from wells drilled with WBM. In 2014, the Quanta Geo service was introduced (Figure 17).14 In design-ing the new tool, engineers used button elec-trodes that function in a different manner compared to those used in WBM imagers.

To determine the formation’s electrical con-ductivity, imaging tools in WBM inject current directly into the formation from the button elec-trodes. Because OBM and mudcake act similar to electrical insulators, current is impeded from going into the formation and from returning to the tool. To overcome this dilemma, the button electrodes of the Quanta Geo service establish capacitive contact with the formation by sending current at much higher frequencies—in the MHz range—than the current used in WBM imagers. Imagers designed for WBM operate with currents in the kHz range.

When OBM is used for drilling, rather than acting as a true insulator, the fluid and the mud-cake actually behave like a lossy dielectric. A dielectric is a material that acts as a poor con-ductor of electric current and impedes current flow. Although a dielectric has properties similar to those of an insulator, it differs in that the impedance—defined as the resistance to flow of an AC composed of resistive and reactive compo-nents—of a dielectric decreases inversely with increased frequency.

Figure 16. Sonde and pad of the OBMI service. The OBMI tool (left ) has four pads. Each pad (right ) has two rows of sensor buttons. Current is emitted from the sensors and returns to the electrodes above and below the sensors. Because of the limited borehole coverage of this design, the OBMI2 tool—consisting of two tools stacked and offset by 45°—was developed (not shown).


Figure 17. The Quanta Geo photorealistic reservoir geology service. This tool has two sonde sections; each section has four pads oriented at 90°, and the two tool sections are offset by 45°. Each spring-mounted pad is fully independent and can swivel ±15° around the tool axis. The mechanical design allows the tool to be logged in an up or down direction. Azimuthal coverage in an 8-in. borehole is 98%.


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Conversely, formations act like resistors, and the resistance remains fairly constant over a large resistivity and frequency range—up to a point. There is an upper limit to the frequency of the current at which the contribution from the permittivity of the formation becomes non-negligible. Permittivity is a measure of how an electric field affects, and is affected by, a dielec-tric medium. Above the critical frequency, per-mittivity of the formation combines with the

dielectric properties of the mud system. Below the frequency upper limit, the permittivity of the formation is negligible and frequency-related changes in impedance measured by the tool arise from the mud and mudcake proper-ties. Design engineers correct for the contribu-tion to the impedance measurement from the mud and mudcake by using the phase difference between the signals passing through the forma-tion and the signals passing through the mud

and mudcake at two frequencies. During pro-cessing, the analyst can determine which fre-quency provides the optimal response.

The Quanta Geo service outputs an imped-ance measurement seen by the electrodes at the two separate frequencies rather than the conduc-tivity normally measured by WBM-based tools. A consequence of using this technique is that the measured impedance is not directly proportional to the formation resistivity. Computing an invaded zone resistivity (Rxo) from measured data, which is usually available from WBM tool measurements, is not an option.

The Quanta Geo sonde has four pads oriented at 90° and a second set of four pads located below the first set offset by 45°. The fully independent pads are spring mounted and can swivel ±15° around the tool axis as well as longitudinally; this mechanical design allows the tool to be logged in an up or down direction. Azimuthal coverage in an 8-in. borehole is 98%. The tool operates across a resistivity range of 0.2 to 20,000 ohm.m.

Each pad has a horizontal row of button electrodes bounded above and below by guard rings and return electrodes (Figure 18). High-frequency current emitted from each of the 192 buttons capacitively connects to the forma-tion and returns back to the tool. Using two return electrodes provides a symmetrical tool response. The current flowing from each button is mea-sured, and the impedance is computed. This impedance contains both the amplitude ratio

Figure 18. Quanta Geo Pad design. Each of the eight identical pads for the Quanta Geo service has a row of button electrodes surrounded by a guard electrode (left ). Return electrodes are above and below the button electrodes. Two high-frequency alternating currents are forced to flow through the mud and mudcake into the formation; the currents (right , white arrows) return to the upper and lower electrodes, providing a symmetrical response. Current is prevented from returning directly to the tool by the guard electrode. The current flowing from each button is measured and the impedance is computed. This impedance contains both the amplitude ratio between the voltage and current and the phase shift for the two AC frequencies. Tool design provides a vertical resolution of 6 mm [0.24 in.] and a horizontal resolution of 3 mm [0.12 in.].


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between the voltage and current and a phase shift for the two frequencies. The measured impedance is a mix of the formation and the mud responses. Electrode spacing and tool design pro-vide a vertical resolution of 6 mm [0.24 in.] and a horizontal resolution of 3 mm [0.12 in.] Two sam-pling interval rates are available—5 mm [0.2 in.] and 2.5 mm [0.1 in.]

Processed image data from the Quanta Geo service produces photorealistic images never before possible in OBM systems (Figure 19). Log analysts use these high-resolution data to define structural features such as faults and unconfor-mities. Stratigraphic features such as crossbed-ding and foreset beds can be identified; depositional characteristics such as bioturba-tion, clasts and scours can be recognized in the

images. The high-quality images allow interpret-ers to identify natural and drilling-induced frac-tures and quantitatively determine their physical properties. The image quality for data acquired with the Quanta Geo tool is comparable to that of the images available from the FMI-HD service (Figure 20).

Deepwater ApplicationIn the Gulf of Mexico, deepwater exploration offers the potential for significant discoveries. In the search for new sources of oil, operators rou-tinely drill to 30,000 ft [9,100 m] and beyond.15 The cost of drilling a single well is high, and the number of wells drilled into a structure is inten-tionally kept to a minimum. Because of the extreme depths and possible subsalt placement of target reservoirs, the structure and reservoir

architecture may not be as well understood as it is for shallower horizons. For proper placement of the limited number of wells that are drilled to develop these reservoirs, geologists must have a clear understanding of the subsurface geometry. Geologists start with seismic data to develop res-ervoir models, but for fine-tuning the models, dip-meter and image data are crucial.

Image acquisition in deepwater wells must be performed almost universally in OBM drilling sys-tems, and the ability to acquire high-quality images has been challenging. This difficulty is often compounded by the presence of low forma-tion resistivity and little resistivity contrast between beds; the formation signals are small, and the system has little tolerance for measure-ment error or noise.

15. Bloemenkamp et al, reference 14.

Figure 20. Photorealistic examples. Static (left, Track 1) and dynamic (Track 2) images are presented from data acquired with an FMI-HD tool in a well drilled with WBM. Dynamic (right, Track 1) and static (Track 2) images from a well drilled with OBM using data from a Quanta Geo service. These images can be used to identify stratigraphic features and structural dip.


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Older generation OBM imaging tools provided reasonable success for structural analysis although structural dip determination can be difficult in shales that have been altered during lithification and burial. Sedimentological interpretation is usually beyond the limits of these tools.

When high-resolution data are available, geol-ogists can identify textural features from bore-hole images to help them understand the internal structure of thick sediment sections and to define orientable features indicative of stratigraphy. Conventional cores can provide this information; however, because of their prohibitive cost in rig time, acquiring cores in a large number of wells

or over extended openhole intervals is impracti-cal in deepwater projects. The Quanta Geo ser-vice was developed, in part, to address the need for a tool capable of producing photorealistic images in these challenging environments.

To test the imaging capabilities of the Quanta Geo service, a deepwater Gulf of Mexico operator ran the tool in a 97/8-in. wellbore. The well was drilled with a synthetic OBM typically used in the region. The logging toolstring included a dipole sonic tool for determining for-mation mechanical properties. Images were acquired logging down while running in the hole and logging up while pulling out of the hole. The

tool achieved an 80% circumferential coverage of the wellbore.

Thick channel sands are common drilling targets in deepwater exploration. Characterizing these sands, and correctly understanding the stratigraphy, can be illustrated by looking at the information gleaned from the Quanta Geo ser-vice. Interpreters were able to determine that a sequence started with low-energy channel fill followed by a rapid, high-energy influx of mate-rial. Geologists further discovered that what appeared to be a massive sand sequence from standard log interpretation was actually a series of approximately 50 individual depositional

Figure 21. Gulf of Mexico deepwater exploration well. Analysts initially interpreted the sands encountered in a deepwater Gulf of Mexico well as massive channel sands. However, based on interpretation from image data (Track 2), there may have been as many as 50 individual sand bodies. This particular 7-ft [2.1-m] sand interval appears to be uniform; however, the rapid change in dip direction between the bottom and top (Track 3) and the distorted bedding planes (Track 4) are indicative of

a slump fold. The 3D view (right ) clarifies this; the magenta planes show the bedding orientation. The net sand thickness may be much less than it appears in conventional logs and may be disconnected from the rest of the channel sand complex, which will have implications for further field development. This type of information is crucial for development of deepwater reservoirs because operators limit the number of wells drilled. (Adapted from Bloemenkamp et al, reference 14.)


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events (Figure 21). In addition, the paleotrans-port direction was inferred from the images. Structural features—unconformities, faults and high-stress regions—were also clearly visual-ized in the image data. Understanding original depositional conditions and structural geometry aided in planning the optimal program for drill-ing and development within the field.

The Quanta Geo service depth of investigation is an order of magnitude shallower than than that of legacy OBM imaging tools. In an OBM environ-ment, mud filtrate usually flushes free formation fluids from permeable sands in the shallow region from which the Quanta Geo service acquires data. The OBM filtrate filling pores in this shal-low region has a high resistivity value. The rela-tive resistivity measured by the Quanta Geo service in shales relates primarily to the conductive and immovable claybound water. Because shales, which have little intrinsic permeability, are not invaded, the relative resistivity computed from

16. Nelson R: Geologic Analysis of Naturally Fractured Reservoirs 2nd ed. Woburn, Massachusetts, USA: Gulf Professional Publishing, 2001.

17. For more on fractures and hydraulic stimulations: Gale JFW, Reed RM and Holder J: “Natural Fractures in the Barnett Shale and Their Importance for Hydraulic Fracture Treatments,” AAPG Bulletin 91, no. 4 (April 2007): 603–622.

18. Nelson, reference 16.19. For more on image logs used to analyze drilling-induced

fractures and in situ stress direction: Aadnøy BS and Bell JS: “Classification of Drilling-Induced Fractures and Their Relationship to In-Situ Stress Directions,” The Log Analyst 39, no. 6 (November 1998): 27–42.

the Quanta Geo data should be unaffected by invasion and comparable to the shale resistivities measured from other sources. By comparing rel-ative resistivities from this shallow depth of investigation, geologists can obtain an accurate net sand count.

The orientation and geometry of drilling-induced fractures were also identified. Such frac-tures are helpful for establishing the maximum horizontal stress direction, especially in combi-nation with mechanical properties determined from advanced acoustic measurements. Natural fractures, which could rarely be visualized in images from older generation OBM tools, were numerous and easily identified.

Fracture CharacterizationNaturally fractured reservoirs make up a signifi-cant portion of global oil and gas reserves.16 The presence of fractures and fracture networks adds complexity to reservoir analysis and reser-voir characterization—a complexity that is absent in reservoirs in which the matrix pore space dominates.17 Operators must understand the nature and characteristics of fractures and fracture networks in reservoirs that must be hydraulically stimulated to produce commer-cially. These fracture systems will greatly affect well performance and field development. As such, completion programs and stimulation designs must include the effects of natural frac-tures and fracture networks.

A common fracture description system labels fractures as open, healed and partially healed. Open fractures generally increase reservoir per-meability and offer conduits to fluid flow. During drilling operations, open fractures fill with drill-ing fluid or seal with mudcake.18 Healed frac-tures, also referred to as mineral-filled and closed fractures, are common (Figure 22). After they form, fractures can fill over time with a sec-ondary cementing material, which is often quartz, carbonate or a combination of minerals. Unlike open fractures, healed fractures can impede reservoir fluid flow. However, fracture stimulation programs often reactivate the frac-ture network along healed surfaces. In some cases, drilling alone can reactivate healed frac-tures. Partially healed fractures exhibit varying degrees of open and closed properties.

For identifying and characterizing natural fractures in situ, analysts may use well logs, which are usually integrated with other techniques to develop a macroscopic view of the reservoir. Fractures are often inferred from logging tool responses rather than measured. For instance, analysts may use sonic log data to identify anoma-

lous responses, such as cycle skipping, which may indicate the presence of fractures.

Drilling-induced fractures are frequently observed in newly drilled wells. These fractures result from wellbore failure during drilling and can be caused by a high mud weight that breaks down the formation. Drilling-induced fractures can usually be distinguished from natural frac-tures in well logs because they appear as mostly parallel but incongruent pairs on opposing sides of the wellbore in vertical wells (Figure 23).19 These fractures, which are indicative of the

Figure 22. Healed fracture. This fracture is filled with mineralized material. Although healed fractures such as this one may not contribute to the intrinsic permeability of a reservoir section, these types of fractures may be reactivated during hydraulic stimulations.


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stress profile because they are associated with the direction of maximum principal stress, do not contribute to production.

Another indicator of stress direction is bore-hole breakout, which is characterized by an oval borehole observed in caliper logs. The presence of breakout along one axis is usually an indica-tion of the direction of minimum principal stress. Breakout in one direction combined with the presence of fractures in the adjacent axes are indications of drilling-induced fractures; analysts can infer principal stress orientation based on these features.

Many tools and methods have been developed to detect natural fractures downhole. Some basic methods commonly deployed use seismic, ultra-sonic, sonic, optical and electrical systems.

Geologists can use seismic data to detect frac-ture swarms but not individual fractures. Ultrasonic tools, such as the UBI service, produce full circumferential images of the borehole wall; however, image quality from reflected ultrasonic

pulses is highly dependent on the geomechanical properties and quality of the borehole surface. The best results are achieved in hard formations that have few drilling-induced effects. Dispersion curves from elastic shear waves are also used to characterize fracture systems.20 Tools such as the Sonic Scanner acoustic scanning platform can acquire these measurements on wireline. LWD options include the SonicScope multipole sonic-while-drilling service. Optical methods include downhole cameras and televiewers; mud-filled environments are difficult to image using optical devices, however. The most common and effec-tive method for fracture evaluation involves high-resolution electrical measurements. The FMI-HD and Quanta Geo services are examples of wireline logging tools and the MicroScope HD tool is an LWD example.

Until recently, fracture characterization using imaging logs for wells drilled with OBM posed problems for analysts. The spatial resolu-tion for tools such as the OBMI and OBMI2 ser-

vices is about 1 cm, which is sufficient for structural dip determination. However, the pho-torealistic images provided by the Quanta Geo service redefines OBM imaging both in quality and resolution (Figure 24). This was demon-strated recently in a horizontal well drilled in an unconventional reservoir that has vertical and subvertical fractures.

Finding Fractures in Unconventional PlaysThe advances in technology that enable oil and gas operators to exploit unconventional resources such as organic shales, coalbed methane and tight rocks include horizontal drilling and hydraulic fracture stimulation. The presence of natural fractures, and the activation of those fractures using hydraulic stimulation, is one of the key components for success. When operators lack a thorough understanding of the fracture networks in place, drilling operations, comple-tion designs and stimulation programs may not be optimal.21

For wells in which the presence of fracture networks is key to success, completion and stimu-lation designs that properly leverage the fracture systems can mean the difference between com-mercial success and failure. Many of these wells are drilled using OBM systems, which makes frac-ture characterization difficult because an OBM-filled open fracture will have a resistivity signature similar to that of a mineral-filled healed fracture. The Quanta Geo service identifies fractures and may, in some cases, be able to differentiate open fractures from healed fractures.

Southwestern Energy drilled a vertical evalu-ation well in an unconventional resource play in the Northeast US. The zone of interest was drilled using OBM and had an 81/2-in. borehole. The well was used for data acquisition to understand and characterize the reservoir and would then serve as a pilot hole for a lateral well. A UBI tool was run in addition to the Quanta Geo service, and the images from the two sources were compared.

The increased circumferential coverage of the borehole and its enhanced resolution allow the Quanta Geo service to overcome limitations inherent in older generation OBM imaging tools. Analysts can usually detect high-angle fractures intersecting the borehole that might not be obvi-ous using data from other tools.

Data from the Quanta Geo service can also be processed to evaluate tool standoff, a measure of the degree of pad contact with the borehole wall. The standoff image, generated using an advanced inversion processing technique, is then used to correct the image for standoff effects.22 These data can also be used to generate a sensor stand-

Figure 24. Images from a naturally fractured zone. In this image from a well drilled in the Northeast US, the gamma ray log (Track 1) is indicative of a shale. The dynamically generated image (Track 2) has been interpreted by a geologist. The fractures on the image appear to be dipping to the south; however, these data have not been corrected for borehole drift and tool position. The high-angle fractures are actually dipping to the NNW (Track 3), as indicated by the modified tadpoles, which have been corrected to give true dip. Their strike is ENE–WSW, which the stereonet plot clearly indicates; stereonet plots stack interpreted data to simplify trend identification. The uninterpreted image (Track 4) is presented for reference.


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off image, which is a quality indicator of the image generated by the tool and can reflect the presence of hole rugosity. Because the measure-ment comes directly from the borehole wall, the image resolves both geologic and drilling-induced features. Another application of the standoff image is the identification of open fractures.

Tools such as the UBI service are often used to determine fracture status. Images derived from the UBI service result from variations in the acoustic reflectivity of the inner surface of the wellbore. These images are sensitive to minor changes in the surface. Open fractures filled with fluid can be distinguished; however, healed frac-

tures filled with a material that has an acoustic impedance similar to that of the surrounding for-mation are invisible to the tool.23 This application has been used to infer open or closed fractures.

Log analysts compared acoustic reflection images with those from the Quanta Geo service, including the standoff image (Figure 25). The interpreters were able to identify fractures in the dynamic and static images. The inverted standoff images clearly identified the open frac-tures, but healed fractures were not resolved. By inference, fractures observed only in the standoff image are considered open; those not seen in the standoff images are considered closed or very small open fractures. For geolo-gists, the ability to characterize the state of fractures in downhole conditions from Quanta Geo data has great implications for well completion designs and field development.

The Future of ImagingElectrical coring was a vision of the early develop-ers of wireline logging tools. The pictures painted by the latest generation of photorealistic imaging

tools in some ways approach that vision. Logging tools will never completely replace conventional coring because cores provide information that extends beyond visual analysis. However, new techniques and technologies are giving geologists insights into downhole conditions in both WBM and OBM wells never before possible.

The answers from these technologies help guide developers of completion programs to focus on the sweet spots in individual wells and also provide insight into reservoir properties on a scale previously unattainable in OBM-drilled wells. When combined with information from other petrophysical measurements and surface and subsurface seismic data, these new approaches will enable operators to effectively evaluate their resources, optimize development programs and, in some cases, move marginal plays into the realm of commerciality. From a financial standpoint, the resulting picture will be worth more than mere words alone. —TS

Figure 25. Differentiating open and healed fractures. Sinusoids indicate fractures in the static image (Track 1) and the dynamic image (Track 2) from the Quanta Geo service. Modified tadpoles and the stereonet plot (Track 3) indicate the high angle of the fractures and their NNW–SSE strike direction. Determining the fracture status—open or closed—from these images alone is not possible. The UBI image (Track 5) shows many superficial

drilling artifacts—the effects of drilling and backreaming—and vertical scratches from previous logging runs. The fracture at about 977 ft is visible on all images. Two fractures at about 982 ft do not appear in the UBI image or the inverted standoff image (Track 4). These fractures are likely closed. By inference, the standoff image may be a useful indicator of the status of fractures—open and closed.


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20. For more on sonic data used for fracture detection: Haldorsen JBU, Johnson DL, Plona T, Sinha B, Valero H-P and Winkler K: “Borehole Acoustic Waves,” Oilfield Review 18, no. 1 (Spring 2006): 34–43.

21. For more on geosteering and horizontal drilling: Amer et al, reference 4.

22. For more on using standoff images for fracture characterization: Chen et al, reference 10.

23. For more on using images from the UBI service for fracture characterization: Ellis D, Engelman B, Fruchter J, Shipp B, Jensen R, Lewis R, Scott H and Trent S: “Environmental Applications of Oilfield Technology,” Oilfield Review 8, no. 3 (Autumn 1996): 44–57.

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