Quantitative Characterization of a Naturally Fractured Reservoir Analog

Research Paper

GEOSPHERE Quantitative characterization of a naturally fractured reservoir GEOSPHERE; v. 14, no. 2 analog using a hybrid lidar-gigapixel imaging approach

doi:10.1130/GES01449.1 Kivanc Biber1,2, Shuhab D. Khan1, Thomas D. Seers3, Sergio Sarmiento4, and M.R. Lakshmikantha4 1 14 figures; 7 tables Department of Earth and Atmospheric Sciences, University of Houston, 4800 Calhoun Road, Houston, Texas 77004, USA 2Statoil North America, 2107 CityWest Boulevard, Houston, Texas 77042, USA 3Department of Petroleum Engineering, Texas A&M University at Qatar Engineering Building, Education City, Doha, Qatar 23874 CORRESPONDENCE: kbiber@uh.edu 4Repsol USA, Technology Hub, 2455 Technology Forest Boulevard, The Woodlands, Texas 77381, USA

CITATION: Biber, K., Khan, S.D., Seers, T.D., Sarmiento, S., and Lakshmikantha, M.R., 2018, Quantitative characterization of a naturally fractured ABSTRACT 1. INTRODUCTION reservoir analog using a hybrid lidar-gigapixel imag ing approach: Geosphere, v. 14, no. 2, p. 710–730, The inability to accurately resolve subseismic-scale structural discontinui Understanding the orientation distribution and spatial configuration of doi:10.1130/GES01449.1. ties such as natural fractures represents a significant source of uncertainty natural fractures is important because these structural discontinuities signifi- for subsurface modeling practices. Fracture statistics collected from outcrop cantly influence the behavior of many oil and gas reservoirs. As such, they Science Editor: Raymond M. Russo Associate Editor: Francesco Mazzarini analogs are commonly used to fill the knowledge gap to reduce the uncer- impact fluid flow (e.g., Wilson et al., 2011b) and geomechanical state of the tainty related to fracture-induced permeability anisotropy. The conventional reservoirs (e.g., Heffer, 2012; Couples, 2013). Therefore, it is a common prac- Received 31 October 2016 methods of data collection from outcrops are tedious, time consuming, and tice to include the contribution of fractures into static and dynamic reservoir Revision received 23 September 2017 often biased due to accessibility constraints. Recent advances in virtual out- models and simulations (e.g., Wilson et al., 2011a, 2011b; Bisdom et al., 2014). Accepted 29 November 2017 crop-based methods in fracture characterization enhance conventional meth- Recent advances in the 3D seismic imaging and analysis now allow the con- Published online 12 January 2018 ods by streamlining data collection and analysis. However, certain limitations struction of geometrically accurate models with depositional and structural and challenges exist in virtually obtained fracture data sets. The ability to architectures constrained at resolutions of tens to hundreds of meters (Caers identify fractures that are both exposed as lineations and as planes from a et al., 2001). However, most structural heterogeneities that can significantly im- digital outcrop model depends heavily upon the fidelity and resolution of its pact reservoir behavior manifest at scales below the conventional subsurface surface display of RGB color, reducing the capacity of light detection and rang- imaging thresholds (~20 m; Mrics et al., 2005). Unobservable structural hetero- ing (lidar) to the resolution of the scanner-attached camera. In the present geneities such as faults, fractures, and compression structures (e.g., stylolites study, we adopted a hybrid approach, combining lidar-based digital outcrop and compaction bands) introduce significant anisotropy within the rock mass models and georeferenced high-quality photomosaics, providing improved resulting in permeability corridors or baffles and/or barriers, with hydraulic texture maps in terms of pixel density compared to maps generated from conductivities typically several orders of magnitude higher or lower than the on-scanner camera images. With this approach, the effects of truncation on surrounding rock mass (Aydin, 2000). digital outcrop models were limited, giving the ability to detect fractures that The use of outcrop analogs to generate geological conditioning data is OLD G would otherwise be aliased from on-scanner camera imagery. The fracture a common strategy employed within reservoir modeling studies (e.g., Enge system developed within the exposures of the Mississippian Boone Forma- et al., 2007; Pranter et al., 2008). Scalable observations can be made from out- tion, an outcrop analog for age-equivalent reservoir objectives in Mississippi crops that are below conventional seismic thresholds, while providing spa- Lime hydrocarbon play, was characterized using conventional and virtual out- tially (especially laterally) continuous data (Jones et al., 2011). However, con- OPEN ACCESS crop-based techniques. To test the fidelity of the virtual fracture extraction ap- ventional methods of outcrop fracture characterization suffer from deficiencies proach, fracture orientation statistics generated from lidar are compared with in relation to their ability to efficiently capture sufficient information about equivalent data sets collected using traditional surveys. The results suggest fracture geometry, intensity, and orientation that can accurately represent the that terrestrial lidar, coupled with referenced gigapixel photomosaics, provide overall characteristics of the rock mass. A typical analysis of fractures in out- an effective medium for fracture identification with the capacity of resolving crop consists of collecting detailed observations manually using window map- fracture characteristics with sufficient fidelity to potentially act as condition- ping or scanline surveys (Priest, 1993). Manual survey techniques, although This paper is published under the terms of the ing data for discrete fracture network models, making it an attractive alterna- precise, are labor and time intensive, with resultant data sets restricted by the CC‑BY-NC license. tive tool for fracture modeling workflows. lack of spatial continuity offered by the sampling domain, due to inaccessibility

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of the upper reaches of most outcrops. Terrestrial laser scanning (TLS; also

known as terrestrial lidar) is a proven close-range remote-sensing technique A B — SB for outcrop studies, which may serve to enrich observations of a geological, geochemical, or geotechnical in nature, through the addition of a geospatial component (Enge et al., 2007; Buckley et al., 2008; Burton et al., 2011; Olariu et al., 2011; Hartzell et al., 2014). The inherent millimeter to submillimeter reso- m lution and accuracy of laser scanning means that a wealth of high-quality data can be collected and analyzed within a relatively short period of time (Buckley 15 et al., 2008; Seers and Hodgetts, 2014). In the present work, we use terrestrial lidar characterization of rock discontinuities that encompasses fracture characteristics such as orientation, size, density, and spacing. We review existing methods, both conventional and — MFI digital outcrop based, to derive important fracture parameters from rock exposures. Building upon these studies, we utilize a combination of terrestrial Sandstone lidar and georeferenced gigapixel photomosaics to map exposures of the Mis- Oolitic grainstone sissippian Boone Formation, an analog for reservoir objectives in the Arkoma Wackestone and/ or carbonate breccia Basin, USA. Cherty wackestone and/or packestone

2. GEOLOGICAL SETTING Figure 1. (A) Generalized lithostratigraphy of Lower Mississippian subsystem in northwest Arkansas (modified after Handford and Manger, 1990; Manger and Shelby, 2000). Red box denotes the stratigraphic position of the rocks in War Eagle quarry. (B) Coarsening upward The outcrop that forms the focus of this study is located at the War Eagle lithofacies observed in the quarry. SB—sequence boundary; TST—transgressive systems tract; quarry, 6 km northeast of Huntsville, Arkansas on Highway 412 (W93°41.227′, HST—high stand systems tract; MFI—maximum flooding interval. N36°7.168′). Approximately 15 m of strata within the upper part of the Carbon- iferous Boone Formation is exposed within the quarry. The exposed strata belong to the lower Mississippian sequence and correspond to carbonate-ramp wackestone, mudstone, and carbonate breccia. Chert is nearly absent through- deposits with varying conditions of energy and depth (Handford and Manger, out the rest of the section. The upper unit (termed as “layer 3”) is ~7 m thick 1990). The range of depositional environments viewed in the War Eagle quarry and only exposed in the east wall of the quarry. It is composed of oolitic grain- has previously been interpreted as being deep-shelf margin to open-marine stones with common occurrence of cross stratification (see Fig. 2 for outcrop shallow-shelf edge settings (Liner, 1979). Lisle (1983) recognized that upper- photos). This interval represents the periodic establishment of higher-energy most parts of the outcrop belong to Short Creek Oolite member (Fig. 1). On the environment within a low-energy carbonate shelf setting (Zachry, 1979). other hand, virtually no oolites are present at the base of the outcrop, suggest- The outcrop exposed in War Eagle quarry belongs stratigraphically to the ing a more characteristic “Boone” style of deposition. Stratigraphically, the upper Boone Formation. The Boone Formation and its lateral Osagean equiv- outcrop represents a typical Upper Boone succession with penecontempora- alents dip west toward the Oklahoma-Kansas boundary, where they form the neous chert layers and nodules at the base and the Short Creek Member with historically targeted reservoir intervals in the Mississippi Lime hydrocarbon massively bedded and persistent oolitic strata within the uppermost parts of play. Potential reservoir facies observed in the outcrop are the grainstones, the quarry wall exposures. These units are overlain unconformably by Ches- grain-rich packstones, and some sandstones. The observed vertical thickness terian Hindsville Formation, which is observed at the top of the eastern quarry of the grainstones in the quarry walls is ~4 m. Laterally, these facies are trace- wall. This unit contains cross-laminated, fine-grained sandstones and angular able as far as the quarry exposure permits. Other observed sites in the vicinity chert clasts, possibly derived from the underlying Boone Formation. had similar stratigraphic arrangements with similar ratios, which may be of Three lithofacies can be identified from the War Eagle quarry outcrops. significant volume. Deposited on a carbonate platform as sheets, subparallel The contacts between the lithofacies are observed to be sharp where the en- to the shoreline, it is expected that lateral continuity in both strike and dip di- tire quarry is arranged in coarsening upward cycles (Fig. 1B). The lower unit rections would be in the order of kilometers, similar to the modern grain-rich (termed as “layer 1”) is ~12 m thick and composed of wackestone and pack- shoals (Fig. 3, Sivils, 2004). It is suggested that the Boone Formation formed stone with crinozoan and bryozoan fragments scattered in a matrix of micro- in a shallow-dipping, carbonate-dominated depositional system that can serve spar (Lisle, 1983). Anastomosing bands and nodules of cherts and stylolites are as an outcrop analog for age-equivalent, hydrocarbon-producing sequences abundant. The middle unit (termed as “layer 2”) is ~5 m thick and composed of in the subsurface (Shelby, 1986). Cyclicity-related reservoir-seal lithofacies

A C Oolitic N N F N grainstones Layer 3 Stylolite bedding Layer 2 X-laminated, oolitic grainstones 1 m

D Sandstones N Fracture Layer 1

Chert nodule Subvertical 4m 1 m 10 cm fractures B E

Nodular chert N N

Bedded chert

~2 m 2 m

Figure 2. Outcrop photos illustrating the types of fractures and lithologies in the quarry. (A) View of the east quarry wall exposing the three lithological layers. (B) Conjugate fractures on top of the south wall are exposed in plan view. (C) Cross-laminated oolitic grainstone exposed on the east wall is diagnostic of layer 3. (D) Sandstones exposed on top of east wall unconformably overlying the oolitic grainstones suggesting the presence of a sequence boundary. (E) View from the north wall exposing bedded and nodular chert occurrence, diagnostic to layer 1. (F) Horizontal stylolite bedding is commonly observed in cherty layer 1. These may act as flow barriers. Vertical, healed, and confined fractures are commonly observed in between the two layers bounded by stylolite bedding.

types can be used as a basis for reservoir comparison and modeling in the with respect to the evolution of the subsurface stress field. Variations on the subsurface. These may include age-equivalent reservoirs in the midcontinent fracture orientations and densities observed on the outcrop and how they oil province such as those in Mississippi Lime hydrocarbon play (Keith and manifest within sedimentary units exhibiting different mechanical properties Zuppann, 1993, Fig. 4). In addition to the depositional setting and age equiv- (e.g., Young’s modulus) could provide insights into the structural character of alency, the Boone Formation possibly could potentially provide some insight equivalent subsurface reservoirs.

distance for the images captured with the TLS-mounted single-frame Nikon D800 camera setup varied between 3.7 and 8.6 mm. Digital outcrop data at the War Eagle quarry were collected over two field days during Spring season under similar lighting conditions. To minimize oc- clusions (scanning shadow) and maximize draping accuracy, point-cloud and image data were acquired from multiple positions (11 and eight positions, respectively). The distance to the outcrop for all scanning and imaging positions ranged between 15 and 35 m. The two data sets consisted of a total of 11 scan positions, which were merged into a common coordinate system with the aid of a network of retro-reflective targets placed on the quarry walls. Furthermore, lidar-derived point clouds were aligned to the magnetic north with the aid of the scanner’s internal compass and clinometers, thus enabling measurements from the outcrop model to be compared directly with manually collected orientation data (internal compass accuracy is typically 1°; RIEGL Laser Measurements Systems, 2011). Additional reflectors were used to mark the manual scanline transects enabling the virtual scanlines to be accurately matched (e.g., Seers and Hodgetts, 2014). The resultant point cloud contained over 3 × 108 points with an average sampling interval of 1 mm. Furthermore, a total of 3600 high-resolution panoramic images were simultaneously collected using the robotic camera stage, required for the generation of gigapixel texture maps. The use of exposures in the War Eagle quarry is motivated by the favorable geometries that the quarry walls exhibit in terms of digital outcrop data collection and analysis. For example, varying wall orientations within the quarry reduce Terzaghi discontinuity orientation bias (Terzaghi, 1965), and the planar, near-vertical and horizontal exposures limit data shadows and expose an extensive network of subvertical fractures.

Figure 3. Map showing the paleogeography and lithofacies distribution of the Lower Mississip- pian. The location of the study outcrop is indicated by the star that falls within the Shelf Margin (redrawn after Lane and De Keyser, 1980). 3.2. Digital Outcrop Model Construction

The construction of outcrop models follows a similar approach presented 3. METHODOLOGY by Buckley et al. (2008), Fabuel-Perez et al. (2010), and Rarity et al. (2014), with modifications to accommodate the registration of photomosaic imagery, 3.1. Data Acquisition which is explained in the following section. The workflow generally comprises four phases: (1) the merge and alignment of individual scan data from the two The terrestrial lidar platform used in the study was a RIEGL VZ-400 terres- surveys; (2) cleaning, consolidation, and optimization of scan data; (3) regis- trial laser scanner with an operable range of ~2–400 m, a ranging accuracy of tering external panoramic imagery to the scan data; and (4) triangulating and 5 mm, and precision of 3 mm (RIEGL Laser Measurements Systems, 2011). A texture mapping digital outcrop surface meshes with referenced high-resolu- Nikon D800, 36.8-megapixel digital camera with a calibrated 20 mm lens was tion photo-imagery. also mounted on the scanner, enabling the simultaneous capture of auto-ref- Merging of point clouds was achieved using the Multi-Station Adjustment erenced imagery. In addition, the laser scanner data were supplemented using module within RiSCAN PRO software package (RIEGL Laser Measurements off-scanner high-resolution panoramic images captured using a robotic cam- Systems, 2011). First, individual scans from the first survey were coarsely era stage (Gigapan EPIC Pro). A Nikon 3100 digital single-lens reflex camera aligned using manually identified common tie points. Finally, all 11 scans were equipped with a Sigma 500 mm variable-focal-length telephoto lens was used co-registered into a common coordinate frame using a global iterative closest to acquire gigapixel panoramic images (Fig. 5). The range to imaging targets point algorithm. The root mean square error of the final alignment between all varied between 15 and 35 m, which resulted in ground sample distance be- 11 scans was ~3 mm. Subsequent to the merger of scan data, operations were tween 0.4 and 0.9 mm in the object space. In comparison, the ground sample conducted to clean the non-outcrop artifacts (e.g., vegetation and boulders on

Figure 4. Mississippian rock exposures in the quad-state area of southeast Kansas (KS), southwest Missouri (MO), northeast Oklahoma (OK), and northwest Arkansas (AR). The star depicts the location of the study outcrops at War Eagle quarry. Anadarko and Arkoma Basins host age-equivalent and genetically related reservoirs to the studied rocks. Inset map: States of continental United States.

the ground), consolidate all data to a single working data set, and optimize onto the polygonal mesh using texture mapping, creating a photorealistic re- the resultant data set to reduce computational expenditure. This was achieved constructed surface displaying dense RGB texture patches derived from the by resampling the point cloud in an effort to reduce the areas of excessively Gigapan robotic stage. high and low point densities. Five-millimeter point spacing for the point clouds proved to be sufficient to resolve the discontinuity features of interest, with the mapped photomosaic imagery providing a medium through which millimeter 3.3. Registration of Gigapixel Imagery to Lidar Data to submillimeter fine-scale discontinuities could be identified. Since registration of the gigapixel imagery to scan data was a signifi- Because panoramic images were taken independently of the laser scanner, cant effort within this study, the details of this procedure are given in the they require registration to the lidar data. Registration of an image requires subsequent section. Following the registration of panoramic images, the final establishing common points between the image and point-cloud data. Using step of construction of a photorealistic outcrop model was triangulation and these common points, external camera position and orientation with respect texture mapping with the georeferenced gigapixel images. A Delaunay tri- to the lidar coordinate system can be calculated. A network of control points, angulation method was used to generate a triangulated irregular network which included retro-reflective targets placed on the outcrop segments within (TIN) from the cleaned and edited point-cloud data. An optimization proce- reach and selected natural targets such as sharp corners of rocks, formed the dure was then applied, eliminating the appearance of sliver tetrahedral (e.g., basis of the registration. RIEGL’s RiSCAN PRO software package was used for Seers and Hodgetts, 2014). Finally, the registered photomosaics were draped the panoramic image registration task.

A TLS-mounted B Nikon D80 Nikon 3100 camera with Sigma 500 mm lens VZ-400 TLS

Gigapan motorized mount

Figure 5. Pictures showing the instrument setup of (A) RIEGL VZ-400 laser scanner and (B) Gigapan robotic camera mount. Field computer

Most common photogrammetric packages employ corner-point projection tortion was not modeled within the photogrammetry package because it was when transforming 2D image coordinates to 3D lidar coordinates under the assumed to be removed during the stitching process. Using the established tie assumption of Cartesian image coordinates. However, the imaging geome- points and the simplified pinhole camera model, the position and orientation try of the Gigapan platform can best be approximated by a spherical projec- of the panoramic images were calculated. Qualitative confirmation of calcu- tion plane, in which the focal point does not change, while the viewing angle lated camera position was done via captured lidar scans of the camera setup. changes as the robotic arm rotates around a fixed axis. Microsoft’s Image Com- It should be noted that the registration of panoramic photomosaic imagery posite Editor (ICE) panoramic imaging software was used to stitch individual is imperfect in nature without applying the correct mathematical model for images. A spherical projection plane was used to minimize distortions inherent multicomponent stitched imagery with shifted pose (e.g., Schneider and Maas, to panoramic imaging. The resultant images were treated as single-frame im- 2006). However, the close proximity to targets and applying distortion remov- ages where a pinhole camera model can be used. ing algorithms to the imagery prior to stitching gave satisfactory registration The photogrammetry package used in this study (RiSCAN PRO) employs results, on the order of a few mm to a few cm in the object space (Fig. 6). pinhole camera models similar to the one used in the Open Source Computer An advantage of this method is that the panoramic photomosaics offer sig- Vision Library, OpenCV (www.opencv.org). The calibration parameters defin- nificantly greater pixel density on the outcrop surface compared to scanner- ing a pinhole camera model consist of intrinsic and internal parameters. Intrin- mounted camera images captured from equivalent ranges. Some fractures sic parameters refer to the camera sensor properties that are supplied by the manifest themselves on the outcrop surface without topographic expression. manufacturer. Internal parameters are used to describe an ideal pinhole cam- These discontinuities are typically truncated from the analysis of a lidar- era and consist of focal length, center of projection, and optical lens distortion derived point cloud or tetrahedral mesh data set (Seers and Hodgetts, 2014). coefficients. Focal length parameter is recorded in the image file, and center of The combination of high-resolution panoramic imagery with lidar-derived projection was assumed to be the physical center of each image. This is a good geological surface reconstructions allowed the recognition of fractures with approximation because of the spherical imaging geometry. Optical lens dis- no discernable topographic expression (Fig. 7).

Figure 6. An example of gigapixel imagery showing the registration accuracy. Red markers—tie points selected on the image; blue markers—corresponding lidar tie points projected onto the image using the parameters discussed in the text. Standard deviation for this scene was ~33 pixels (9 mm). Tie points 1–5 and 7 are retro- reflective stickers, whereas tie point 6 is a natural target easy to identify in point- cloud data (corner of a rock).

3.4. Fracture Data Collection sures. The locations of the manual measurements were registered on the lidar scans in order to compare measurements between the digital outcrop-based The data used in the analysis of this study consist of fracture attributes col- approach and the manual surveys. It should be noted that a direct comparison lected with compass and smartphone and extracted from TLS-derived surface of all fracture parameters between manually and virtually obtained data sets reconstructions. In plan view, the quarry walls form a near-continuous rectan- was not feasible within the scope of this study. The slow data acquisition rate gular perimeter (Fig. 8), meaning that subvertical fracture sets that are oriented inherent to manual surveys and limited number of fractures that were acces- parallel to one exposure should be manifested upon the adjacent wall, miti- sible at the base of the quarry restricted the comparison to be made only for gating the effects of orientation bias. The quarry walls can be examined from the orientation statistics. almost a 360° range of viewing angles, thus unlocking the potential to access a 3D fracture data set. Rock discontinuities were measured by a traditional manual scanline method (Priest, 1993) with a geologic compass (such as Brunton) 3.5. Extraction of Fracture Orientation from Point Clouds and a smartphone (such as Apple iPhone 5s with SMT 3-axis gyroscope and Bosch Sensortech BMA220 3-axis accelerometer; application used: GeoID by In the present work, the fracture orientation analysis from terrestrial lidar Sang Ho Lee, Engineering Geology and GIS Lab.), as well as a terrestrial laser point clouds involved two approaches: manually guided and semi-automated. scanner (manual guided and semi-automatic). First, discontinuities were mea- Manual analysis consisted of direct extraction of fracture attributes through sured along several scanlines with varying lengths between 20 and 60 m along supervised digitization of fracture planes. This was achieved by manual map- the four quarry walls with a geologic compass and a smart phone. The proce- ping of a fracture plane from the point-cloud data by selecting points that rep- dure for all the scanline surveys followed broadly that of Priest (1993). With the resent a rock discontinuity surface and calculation of normal vector of the best- intention of reducing orientation bias, seven scanline surveys were conducted fit plane through the selected points (Fig. 9). Dip and dip direction of the planes on exposures with varying orientations (see Fig. 8 for locations of scanlines). represented by these normal vectors are then calculated using Eigen analysis. Fractures exposed in plan view on the quarry floor were also recorded (e.g., High-resolution laser scans, colorized with the gigapixel photomosaics, guided “Floor” sampling domain in Fig. 8B). A total of 156 orientation measurements the operator in interpreting the fractures. The entire digitization was carried were recorded from all manual scanline surveys, including the plan-view expo- out by a single interpreter to avoid variability due to influence of different

and Henk, 2015), whereby the normal vector of the least-square plane for a A 1X group of neighboring vertices is approximated by the eigenvector corresponding to the minimum eigenvalue of their (Eigen) decomposed covariance matrix (see Fernández, 2005; Seers and Hodgetts, 2016a, for more in-depth discussion). Thus, the orientations of the normal vectors to the planar scanned surfaces were calculated via an Eigen analysis of the covariance matrix of a local 4X point neighborhood (Metzger et al., 2009). After the orientations of the planar surfaces have been calculated, artifact non-discontinuity planar surfaces (i.e., those associated with quarry walls and bedding planes) were removed (Fig. 10). Best-fit planes representing the remaining points are produced and ana- 8X lyzed similar to the manual guided technique explained above. An unsupervised classification technique, k-means clustering, was then used to identify N the orientation distributions of the main fracture sets. 50 cm 10 cm 2 m

B 1X 3.6. Extraction of Fracture Trace Length from Digital Outcrops Although point clouds or surface reconstructions enable fracture set distributions to be rapidly determined, the reliance upon point-cloud or surface reconstruction as a medium for fracture identification has a disadvantage. 4X Some fractures that manifest on the outcrop surface as a linear trace without any topographic expression will not be detected upon the mesh or point- cloud data set (Seers and Hodgetts, 2016a). Therefore, co-planarity of the outcrop surface may not be diagnostic of the fractures, resulting in an under 8X estimated view of the analyzed fracture network. To account for potential

N bias arising from the omission of such fracture traces, we used gigapixel textured meshes generated from Gigapan-captured photomosaics. The 50 cm 10 cm 2 m high-resolution nature of gigapixel texture maps allowed interpretation of healed fractures that generally do not have enough surface expression to be Figure 7. Digital outcrop model examples comparing the resolution of the texture maps gener- resolved by most TLS surveys. The interpretation of fracture traces from the ated using (A) the terrestrial laser scanning (TLS)–mounted Nikon D800 (36 megapixel) digital camera and (B) Gigapan robotic camera mount equipped with a Nikon D3200 digital camera. Im- textured surface reconstructions was manual in nature, with features digi- ages were exported from a 3D visualization tool with different zoom levels. Note that gigapixel tized using CAD-based tools (e.g., ArcGIS ArcScene and RiSCAN PRO). Simi- textures in (B) stay sharp well beyond the 8× zoom level, whereas on-scanner camera textures lar to manual fracture orientation identification, a single operator interpreted become pixelated. Arrows point to a fracture obstructed from the on-scanner camera texture in (A), whereas it is clearly distinguishable within the gigapixel texture map in (B). fracture traces. Seven sampling domains were selected for detailed window mapping, required for the derivation of fracture abundance. Window sampling regions operators (Scheiber et al., 2015). The manual digitization was limited to the were selected based upon the quality of the exposure, outcrop orientation outcrop areas with good exposure and lidar coverage from multiple scan po- (to minimize orientation bias), and the localized occurrence of the lithological sitions. A homogeneous sampling throughout the quarry walls was aimed to units present at the War Eagle quarry field site. With the intention of reducing be achieved; however, certain surficial artifacts such as vegetation and staining data loss due to not interpreting all of the fractures, a lower cut-off length of prevented reliable interpretation of discontinuities. Areas with such character- 10 cm was used based on preliminary analysis of fracture trace data. Attention istics were neglected. was given to make sure the cut-off size was distinguishable using the TLS- The second method for digital discontinuity orientation analysis was mounted camera images. The operator interpreted fracture traces bigger than semi-automated. It was based on extracting quasi-planar discontinuity sur- the cut-off length within the selected areas. Fracture trace-length distributions, faces presumed to be fracture planes, bedding planes, and faults (e.g., spacing, and intensity were then calculated using the digitized traces in these Sturzenegger and Stead, 2009). This was achieved using an orientation tensor areas. A summary of the parameters calculated in this study and the methods analysis (e.g., Woodcock, 1977; Fernández, 2005; García-Sellés et al., 2011; Laux of calculation are presented in Table 1.

Figure 8. (A) Regional map illustrating geological and selected physiographic provinces of Arkansas and adjacent areas. Red star indicates the location of the study area. (B) Aerial view of the War Eagle quarry. Green lines depict the various scanline orientations, which also encom- pass sampling domains. Some large-scale (>5 m) fractures are exposed in plan view over the south quarry wall (e.g., Fig. 2B). SL—scanline. A

3.7. Data Analysis

3.7.1. Fracture Set Delineation and Orientation Statistics

Robust analytical techniques were employed to delineate fracture sets derived from both manual (i.e., with compass and smartphone) and virtual (i.e., extracted from digital outcrop models) orientation data sets. Initial efforts focused on qualitative assessment of field observations of large (>1 m) and systematic fractures, using the stereographic projections. In addition, K-means clustering algorithm was used to define fracture set membership. Statistical parameters of spherical distributions of fractures derived manually and virtually are compared to validate the suitability of digital data sets. Direction of vector sum (υ), degree of preferred orientation (R%), shape parameter (kˆ), and cone of confidence were calculated for the corresponding fracture arrays (Table 2).

3.7.2. Fracture Trace Length

Fracture size exerts significant control over the gross network connectivity B (Priest, 1993) and is a critical parameter for the generation of stochastic discrete fracture networks. In an attempt to estimate fracture size, fracture traces were identified by supervised digitization in selected sampling domains on the digital outcrop model (by window mapping). Using the previously obtained orientation statistics, fracture length distributions were investigated on a set by set basis. Probability plots were constructed for individual sets and used to assess the goodness of fit of measured distributions to empirically observed trace-length probability densities (log normal: e.g., Mayer et al., 2014; expo- Fig. 8 nential: e.g., Robertson, 1970; hyperbolic: e.g., Segall and Pollard, 1983). In order to determine the significance of a trace-length population conforming to a specific distribution, the Kolmogorov-Smirnov (KS) test was performed. If the significance value of the test result is less than 0.05, the null hypothesis that the data comes from a specific distribution is rejected (assuming the optimally fitted distribution has the significance value of 1).

3.7.3. Fracture Intensity and Spacing Fractures Different fracture abundance parameters can be obtained using different sampling spaces (i.e., 1D and 2D), discontinuity dimensionality (i.e., count, trace length, and surface area), and survey methodologies (i.e., scanline, window map, and circular scanline: Dershowitz and Einstein, 1988; Mauldon, 1998;

A 2 m

Figure 9. Example lidar scanning products of the west wall in War Eagle quarry (see Fig. 7B for location). (A) Point-cloud data colorized with gigapixel imagery consist- ing of 7 million points used for manual fracture plane interpretation. (B) Surface reconstruction texturized with panoramic images acquired with the robotic camera stage used for identification of fracture traces (solid blue lines). (C) Fracture panes and traces mapped using data shown in (A) and (B), respectively. Colors of fracture traces represent different fracture sets: green—set 1; red—set 2; and blue—set 3. B 2 m

N Fracture traces n West sampling domai

Fitted 2 m C planes

Planar patches

Figure 10. (A) Near-planar patches identified using the plane extraction method. 22m m Points are color coded based on dip direction of their best-fit planes. Dip direction A)A histogram (near the color scale) indicates the occurrence of two fracture sets centered approximately at 35° and 100° azimuthal directions. (B) Planes that are N best-fitted to the data showed in (A). Sizes of planes are scaled relative to the extent Planes fit to of individual patches while colors are ran- patches domly assigned.

2 m B

Rohrbaugh et al., 2002). Much of the terminology was laid out by Dershowitz length over sampling area). These measurements were taken using trace and Einstein (1988) and Dershowitz and Herda (1992), who defined a number maps digitized within the window mapping regions described above (see

of measures of fracture abundance by the Pxy system, where x denotes the example in Fig. 11). dimension of the sampling region and y the dimension of the feature being Fracture spacing was calculated per fracture set using virtual horizontal measured. In the present study, we documented fracture abundance per scanlines placed on the selected sampling areas because fractures oriented

fracture set as P20 (fracture count over sampling area) and P21 (total fracture subnormal to the constructed scanlines have greater spacing than the true

TABLE 1. PARAMETERS USED TO CHARACTERIZE DISCONTINUITIES AND METHOD(S) OF CALCULATION USING MANUAL AND VIRTUAL DATASETS Parameter Description Method(s) of calculation Orientation Fracture plane orientations Manual, supervised digitization, semi-automatic, on the entire outcrop Set Identification of fracture families K-means clustering analysis Length Trace length of discontinuitiesSupervised digitization in selected areas Spacing Average orthogonal distance between fractures Virtual horizontal scanlines in selected areas

Intensity Count per area (P20) and length per area (P21)Supervised digitization in selected areas

TABLE 2. STATISTICAL PARAMETERS FOR SPHERICAL DISTRIBUTIONS CALCULATED FOR BOTH MANUAL AND VIRTUAL FRACTURE SETS Statistical parameter Description Direction of vector sum (υ) Mean orientation Degree of preferred orientation (R%) Measure of dispersion Shape parameter (kˆ) Distribution measure (Fisher, 1953) 95% confidence Angle of confidence cone for a given confidence level (Fisher, 1953)

value. To remove orientation bias, a correction based on Terzaghi (1965) was (3) Set 3: SE-NW–oriented subvertical discontinuities, conforming to 32% applied. Table 3 summarizes the parameters used in window mapping and of manually, 34% of virtually (supervised), and 37% of virtually (semi- virtual scanline surveys, which yielded the fracture intensity and spacing automatic)–derived total fracture population. calculations. The results of the statistical analysis of manual and virtual fracture orientations are summarized in Table 4. Calculated direction of vector sum (υ) of frac- 4. RESULTS ture populations indicates strong agreement between the manually and virtually derived data sets with a maximum angular deviation of only 6° (set 2) in the 4.1. Fracture Set Delineation horizontal and 3° (set 2) on the vertical.Both measures of variance (kˆ and R%) indicate that set 1 has the least dispersion of all clusters, while set 2 has the Fracture orientations were measured by using manual scanline surveys, greatest dispersion. In addition, measures of variance between the manual and supervised digitization on digital outcrop data (virtual [supervised]), and virtual data sets reveal that all equivalent clusters from virtual data sets have semi-automated attribute extraction techniques (virtual [semi-automatic]). greater dispersion than their manually derived equivalents. The 95% confidence These methods yielded a total of 156, 532, and 5609 individual orientation levels indicate that the sample sets are unlikely to be randomly distributed. measurements, respectively. Stereographic plots of poles to the planes (Fig. 12) indicate the presence of three major sets within both manually and vir- 4.2. Fracture Trace-Length Distribution, Spacing, and Density tually derived fracture populations—namely, north-south, northeast-southwest, and southeast-northwest. Qualitative confirmation of these population A summary of trace length and density statistics for each fracture set is sets is provided by plan-view imagery of top and bottom of quarry walls presented in Table 5 and Table 6, respectively. A total of 1294 fracture traces (e.g., Fig. 2B), whereas (K-means) cluster analysis provided quantitative con- have been mapped on the digital outcrop model, where 27% of total fracture formation, which indicated that 78% and 73% of total fracture population of population were set 1, 44% were set 2, and 29% were set 3. Short but abundant manual and virtual data sets, respectively, fall within 10° (on horizontal plane) set 2 fractures (mean length: 0.38 m) resulted in higher fracture intensity (both

to a cluster center: P20 and P21) values. The analysis of fracture length distributions across the quarry indicates that (1) Set 1: N-S–oriented subvertical discontinuities, conforming to 46% of the trace lengths of all recognized fracture sets approximate to a log-normal manually, 42% of virtually (supervised), and 40% of virtually (semi-auto distribution (Fig. 13). While sets 1 and 3 fractures had similar average lengths matic)–derived total fracture population. (0.48 m and 0.44 m), set 2 fractures were generally shorter (mean length (2) Set 2: NE-SW–oriented subvertical discontinuities, conforming to 22% 0.38 m). Despite their short average lengths, set 2 fractures resulted in the

of manually, 24% of virtually (supervised), and 22% of virtually (semi- highest P20 and P21 densities, whereas fracture sets 1 and 3 have lower and automatic)–derived total fracture population. fairly similar density measurements.

Layer 11

10 Figure 11. An example digital outcrop model panel from east wall showing the interpreted fracture traces. Fractures are color coded based on their set membership. Dashed area denotes the boundary

2 9 of the east sampling domain. Horizontal lines are virtual scanlines separated by 25 cm and used in fracture spacing measurements. Fractures from other sampling domains were mapped and analyzed in the same manner. See Table 3 for sum- Layer mary statistics for sampling domains. 8

7 1

6 Layer

5 Set 1 Set 2 Set 3 Virtual Scanlines

TABLE 3. SUMMARY OF SCANLINE (SL) SURVEYS IN THE WAR EAGLE QUARRY Trend Total virtual scanline length Site Sampling area name* (°) (m)n fractures West SL 1 151.5 81.033 West SL 2 15.4 116.921 West SL 3 126.7 79.118 War Eagle quarry (N36°7.160782′/W93°41.229702′) South SL 1 94.6 64.851 South SL 2 45.9 66.155 East SL 172.2 45.1 129 North SL 76 74.064 *Sampling domains for fracture trace mapping and scanline (SL) surveys coincide with the same outcrop panels. For location of sampling panels, refer to Figure 8.

Compass/Smartphone Virtual (Supervised)Virtual (Semi-automatic) n=156 n=532 n=5609

B % % %

Set 1Set 2Set 3 Cluster center

Figure 12. (A) Manual and virtual (supervised and semi-automatic) fracture plane orientations shown as lower-hemisphere pole projection stereo- plots. Total number of measurements is given by n. Colors represent the identified sets using cluster analysis. The results confirm the occurrence of three major discontinuity sets as described in the text. Note that smaller clusters below 10% of the total fracture population are omitted. (B) Corre- sponding density contour stereonets for data displayed in (A). All three methods indicate the presence of three major discontinuity sets.

TABLE 4. SUMMARY OF ORIENTATION STATISTICS FOR MANUALLY AND VIRTUALLY ORIENTATED FRACTURE SETS Fracture set υ R%2 95% confidence3 kˆ 4 Set 1—Compass and smartphone 2/89 95.5 3.044.9 Set 1—Virtual supervised 4/89 87.5 2.416.1 Set 1—Virtual semi-automatic 0/88 83.0 1.011.8 Set 2—Compass and smartphone 77/8893.53.6 25.8 Set 2—Virtual supervised 72/8582.03.9 11.1 Set 2—Virtual semi-automatic 78/8382.81.3 11.7 Set 3—Compass and/or smartphone 126/89 92.3 3.926.0 Set 3—Virtual supervised 124/90 83.3 3.112.0 Set 3—Virtual semi-automatic 125/90 83.4 1.112.0 1Direction of vector sum. 2Degree of preferred orientation. 3Semi-apical angle of confidence cone at the 95% level. 4Estimated Fisher distribution concentration parameter.

TABLE 5. TRACE-LENGTH STATISTICS (MEAN AND DISTRIBUTION GOODNESS OF FIT) PropertySet 1Set 2Set 3 Mean trace length (m) 0.49 0.41 0.46 (Geometric) Mean trace length (m)0.390.320.37 Kaplan-Meier censoring corrected mean (m)0.600.500.59 Anderson-Darling (normal) P value 0.0005 0.0005 0.0005 Anderson-Darling (exponential) P value 0.0005 0.0005 0.0005 Anderson-Darling (extreme value) P value 0.0005 0.0005 0.0005 Anderson-Darling (lognormal) P value 0.0327 0.0005 0.0505 Anderson-Darling (Weibull) P value 0.0005 0.0005 0.0005

TABLE 6. FRACTURE DENSITY STATISTICS PropertySet 1 Set 2 Set 3 Fracture spacing (m) 2.04 1.08 1.92 Fracture spacing (Terzaghi [1965] corrected) (per m) 1.15 0.67 1.54

P10 (n fractures per m) 0.52 1.03 0.57

P10 (Terzaghi [1965] corrected)/n fractures per m 0.87 1.50 0.65 2 3 P32 estimated using Wang’s (2005) conversion factors (m /m )0.290.570.37

Measured spacing generally overestimates the “true” spacing due to 4.3. Fracture Heterogeneity at the Quarry Scale oblique intersection of fracture planes and scanlines resulting in apparent spacing. Terzaghi correction (Terzaghi, 1965) was applied to the spacing data Fracture networks generally exhibit strong spatial heterogeneity, frequently set to calculate “true” spacing. Bisdom et al. (2014) explored the error asso- linked to faults, folds, stress fields, or lithological trends (Gauthier et al., 2000). ciated with the common application of the Terzaghi method, concluding that These geological drivers control the large-scale flow behavior (Hardebol et al., it accurately calculated true spacing for angles between fracture poles and 2015). The War Eagle quarry is an appropriate case to investigate the possibility scanline less than 70°. We adopted this in our analysis and excluded the data of a fracture heterogeneity driven by the observed lithological trends. Here we from scanlines that were more oblique than this value. Table 7 presents the present fracture heterogeneity as a function of fracture spacing variations using summary of the orientations used in Terzaghi correction. a large number of closely spaced (0.25 m) virtual horizontal scanlines placed

40 3.0 ) alue (m 30 Mean = .481 2.0

y (%) Std. Dev. = .414 N = 271 A 20 equenc ed Lognormal V Fr 1.0

10 Expect

0 0.0 .0 1.02.0 3.04.0 5.0 .0 1.02.0 3.04.0 Trace Length (m) Observed Value (m)

40 3.0 ) alue (m 30 Mean = .379 2.0 Figure 13. Left: Trace-length frequency

y (%) Std. Dev. = .383 distributions for (A) set 1, (B) set 2, and (C) set 3. Right: Q-Q (quantile-quantile) 20 N = 444 B plots for log-normal distributions of trace equenc lengths for (A) set 1, (B) set 2, and (C) set 3. ed Lognormal V Fr 1.0 Data show that the observed population of fracture traces is nearly log-normally

10 Expect distributed.

0 0.0 .0 1.02.0 3.04.0 5.0 .0 1.02.0 3.04.0 Trace Length (m) Observed Value (m)

40 4.0 ) alue (m 30 Mean = .443 3.0

y (%) Std. Dev. = .361

20 N = 290 2.0

C equenc ed Lognormal V Fr

10 Expect 1.0

0 0.0 .0 1.02.0 3.04.0 5.0 .0 1.02.0 3.04.0 Trace Length (m) Observed Value (m)

TABLE 7. SUMMARY OF ORIENTATIONS USED IN TERZAGHI (1965) CORRECTION Fracture West SL 1 West SL 2 West SL 3 South SL 1 South SL 2 East SL North SL and/or scanline (151°) (15°) (127°) (95°) (46°) (172°) (76°) Set 1 (4°) 33 11 57 89 42 12 72 α (°) Set 2 (72°) 77 57 55 23 26 80 4 Set 3 (124°) 25 71 329 78 48 48 Notes: Angles in parentheses represent the azimuthal direction for the scanlines (SL) and the direction of vector sum for the fracture poles. The α values represent the acute angle between the scanline and direction of cluster pole. Numbers in bold represent scanlines that are too oblique to the fracture set being measured, and, therefore, they are omitted (see text for details).

on the digital surfaces of selected detail areas (e.g., Fig. 11). Spacing was calcu- that the local stress fields that developed the observed fracture network in the lated per fracture set by averaging the measurements from multiple scanlines quarry may be parallel or subparallel to the far-field stresses. of the same stratigraphic level. Each bar represents a spacing value (i.e., length The Gigapan approach presented here could also be advantageous as of scanline/count) averaged from multiple scanlines of the same stratigraphic a standalone exercise using structure for motion (SfM) photogrammetry as a level. We applied Terzaghi correction to account for the sampling bias. complimentary 3D data set to TLS. In recent years, significant progress has Uncorrected fracture spacing averaged 0.68 m, 0.85 m, and 0.97 m, whereas been made in photogrammetry applications in fracture network characteriza- corrected spacing averaged 0.49 m, 0.79 m, and 0.88 m for layers 1, 2, and 3, tion. Paired with the availability of open source codes to automatically manage respectively. Although the spacing data seem noisy (values vary locally be- image overlap from each position and applying a pixel-based automatic char- tween two adjacent virtual scanlines), there are some trends that can be seen. acterization, our hybrid Gigapan approach offers researchers much greater For example, the spacing of fracture sets 1 and 3 are similar throughout the ground pixel coverage in their workflows (Hardebol and Bertotti, 2013; Zeeb section, whereas the spacing of fracture set 2 seems to be decreasing toward et al., 2013a, 2013b; Healy et al., 2017). the upper stratigraphic intervals (Fig. 14).

5.2. Pitfalls of Digital Discontinuity Analysis 5. DISCUSSION The success of the virtual fracture characterization method largely depends 5.1. Digital Outcrop versus Conventional Data Collection Approaches on the scan and overlaid image resolution, as well as the size, geometry, and quality of the exposure. The minimum number of observation points to calcu- Terrestrial lidar, coupled with referenced gigapixel photomosaics, pro- late the surface orientation with considerable accuracy depends on the scan vides an effective medium for fracture identification and analysis from rock resolution. Therefore, a prior knowledge of apparent fracture size distribu- exposures. The results of this study confirm previous work that suggests that tions in outcrop would be advantageous to optimize the lidar survey for the the application of high-resolution discontinuity analysis is able to produce extraction of rock discontinuities. It should be noted that the minimum frac- data sets compared to traditional hand-measured surveys (e.g., Becker et al., ture length that can be detected using the Gigapan setup is smaller than what 2014; Seers and Hodgetts, 2014; Laux and Henk, 2015). We also confirmed that can be detected using a single-frame TLS-mounted camera as demonstrated the virtual data sets, especially unsupervised, produced much noisier mea- in Figure 7. However, low-contrast and/or minimal aperture fractures may still surements. be elusive. Manually and virtually collected fracture data sets documented the occur- Fracture orientation measurements obtained from virtual and manual rence of three fracture orientation sets: N-S, NE-SW, and SE-NW. Similar ori- surveys revealed notable differences of dispersion. Calculated measures entation distribution of fractures can be seen in observations made by Hudson of variance (kˆ/R%) indicate a higher dispersion for all of the virtual clusters et al. (2001), Braden and Ausbrooks (2003), Hudson and Murray (2003), Hud- compared to manual data sets. This may be a result of the high level of noise son and Turner (2007), and Chandler and Ausbrooks (2010, 2015) across the obtained within the virtual data sets. Although lidar offers rapid data acqui- wider depocenter of the Boone Formation in northwest Arkansas. This may sition rate, facilitating the collection of statistically significant data sets, the indicate the observations made from the quarry may be scalable up to sev- inherent dispersion due to noise with the digital outcrop-based techniques eral kilometers. Notably, the faults displayed in Figure 4 indicate the presence may impact the modeled network connectivity, resulting in underestimation of three major orientations (N-S, E-W, and NE-SW) which are very similar to of modeled network permeability (e.g., Berkowitz et al., 2000; Seers and the orientations of the fracture sets presented in this study. This may indicate Hodgetts, 2014).

A 13 B 13

12 12

11 11 Layer 3 Layer 3 10 10 )

) Layer 2 Layer 2

9 (m 9 (m r r

8 oo 8 Figure 14. (A) Uncorrected and (B) cor- oo rected fracture spacing measurements rry

rry 7 7 from virtual scanlines. Each bar represents a spacing value (i.e., length of scanline/ Layer 1 qua Layer 1

qua count) averaged from multiple scanlines 6 m of the same stratigraphic level. The gaps m 6

ro in the data are due to clayey contacts not f ro

f exhibiting significant fracture develop- 5 ht 5 ment. See Table 3 for scanline orientations ht ig

ig and total number of fractures.

4 He 4 He

3 3 2 Set 1 2 Set 1 1 Set 2 1 Set 2 Set 3 Set 3 0 0 0.02.0 4.06.0 0.02.0 4.06.0 Average spacing (m) Average spacing (m)

Although noisier than the traditional hand surveys, orientation statistics The ability to identify fractures from a digital outcrop model often depends of virtually derived fractures were in excellent agreement in terms of fracture heavily upon the fidelity and resolution of its surface display of RGB color strikes with those obtained manually suggesting that they can be potentially (Vasuki et al., 2014; Seers and Hodgetts, 2016a). In the present work, high-qual- used as conditioning data for discrete fracture network models. Therefore, ity photomosaic images were draped directly onto triangulated meshes, pro- the virtual discontinuity analysis workflow presented here can be included viding improved texture maps in terms of pixel density when compared to as an alternative to the fracture modeling workflows. It should also be noted maps generated from on-scanner camera images. This is done to limit the ef- that the exposed fractures have their own variability, and therefore the virtual fects of truncation on digital outcrop models. However, the referencing of large method may be accurately sampling the natural variability that the manual quantities of digital images is a considerably large exercise. The extra amount methods might have failed to detect. If we assume this is the case in the of time required to capture and process the optical remote-sensing data de- quarry and that a more accurate description of the fracture network can be creases the advantage in terms of data acquisition rate for lidar platforms obtained with the semi-automatic method, then this implies that a fracture within our hybrid workflow. A more practical solution may be to use calibrated set with similar strike and variable dip angles (Fig. 12) has high probability images of outcrops (at the expense of measurement accuracy) or to adopt a ro- to have high fracture connectivity that may control the bulk permeability of botic camera mount–based automated registration workflow such as the one the medium. presented by Cline et al. (2011). It should be noted that in the present work, the

advantage of gigapixel textures is not exploited fully, given that 36-megapixel with those obtained manually, although lidar-generated orientation clusters scanner images generally provide sufficient pixel density at 20 m for fracture were more dispersed than their traditional counterparts. Conventionally and traces to be readily identified. However, this hybrid lidar-photomosaic ap- virtually obtained data sets suggested three major discontinuity orientations proach holds great promise for cases where lidar data acquisition is conducted that are scalable across a wider depocenter than the studied outcrops. The at a considerable range (i.e., many tens to hundreds of meters; e.g., Hodgetts, hybrid workflow allowed the systematic investigation of fracture abundance in 2013 and references therein). Supplementing mid- to long-range lidar data sets different depositional facies observed in the quarry. Considering the absence with gigapixel overlays, captured as multicomponent photomosaics using a of a tectonic driving force, the fracture network heterogeneity observed in the telephoto lens–equipped digital single-lens reflex camera, could widen their War Eagle quarry suggests variable mechanical properties of the depositional analytical scope, enabling features such as fracture traces and sedimentary units. The success of the fracture characterization using digital outcrop mod- structures commonly aliased from on-scanner imagery to be discerned. els depends largely on the scan and overlaid image resolution. While modern on-scanner digital cameras may provide sufficient pixel density for fracture identification at close ranges, our hybrid lidar-photomosaic approach holds 5.3. Outcrop-Scale Heterogeneity great promise for cases where lidar data acquisition requires a considerable distance to targets. Despite its small extent, the War Eagle quarry fracture network displays a certain degree of heterogeneity. Because of the absence of an obvious tectonic ACKNOWLEDGMENTS control that could be responsible for such heterogeneity, we assume that the We would like to thank Repsol USA for funding and permission to publish the results of this study. observed heterogeneity is due to variations in the mechanical properties of the We are grateful to the members of the Remote Sensing Lab of University of Houston: Ünal Okyay, exposed rock types. Of the three layers exposed in the quarry, layer 1 averaged Virginia Alonso de Linaje, Diana Krupnik, Casey Snyder, Alvaro Berrocoso, Lei Sun, Daisy Huang, Darren Hauser, Preston Hartzell, and Craig Glennie for their help during the field work. We thank the minimum spacing measurements (i.e., maximum P10 density). We interpret Lionel White of Geological and Historical Virtual Models, LLC for providing license for their ArcGIS this as being the result of layered and nodular chert occurrences characteristic extension, Geoanalysis Tools. We are grateful to Dr. Francesco Mazzarini and Dr. Enrique Gomez- to this layer. Figure 14 suggests that the low spacing in layer 1 was mainly Rivas for their valuable contribution during peer-review process. due to set 2 fractures being more abundant in the lower quarry walls because other fracture sets were fairly constant throughout the vertical succession. Set REFERENCES CITED 2 fractures are the short, mainly stratabound fractures that were observed to be primarily hosted in the cherty intervals. 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