Using Outcrop Data in the 21st Century -...

16 Studies in Geology 56

Copyright ©2007 by The American Association of Petroleum Geologists. DOI: 10.1306/1240890St563281

Introduction Outcrops have undoubtedly been key to

the science of geology. However, traditional field work has always been associated with challenges. One is related to safety due to dangerous cliffs. In many cases the cliffs are not only unsafe, but inaccessible.

Another problem is related to visual dis-tortion (parallax errors), making, for instance, correlation of geology across large distances difficult. Precise correlation can also be dif-ficult in the case of covered outcrops (vegeta-tion or talus) or across areas devoid of out-crops (e.g., ravine, subcrop). The most criti-cal limitation, however, is related to 3-D geological modeling, where outcrop data are important to bridge the gap between high-coverage, low-resolution seismic data and low-coverage, high-resolution borehole data. Unlike the subsurface data, outcrop data have traditionally been qualitative and 2-D and thus not in an optimal format for quantitative 3-D modeling.

Using Outcrop Data in the 21st CenturyTore M. Løseth1, John B. Thurmond1, Jan C. Rivenæs1, Ole J. Martinsen1, Carlos Aiken2, and Xueming Xu2

1Hydro Research Centre, Bergen, Norway 2University of Texas at Dallas, Richardson, Texas, USA

Executive SummaryIn this study, our aim is to show that new measuring and visualization techniques can have large implications for the future

application of outcrop data. First, we review different techniques that can be used to collect digital outcrop data. We especially focus on outcrop capture methods such as photorealistic, 3-D digital outcrop models. These models can be rendered with stunning verisi-militude even on laptop computers. We show some of the applications of photorealistic outcrop models, focusing on how the models can be used directly in a virtual reality (VR) environment to produce sophisticated reservoir models with high accuracy. Finally, we show how the captured digital rocks can be visualized synchronously with the modeled ones. The benefits of visualizing the real world and the model at the same time and in an immersive visualization environment are significant, particularly as a platform for multidisciplinary discussions.

A part of this study is based on the Eocene Ainsa Turbidite System in the Spanish Pyrenees. More details of this turbidite system are provided in this Atlas in other papers. The outcrops are located along two flanks of a syncline (the Buil Syncline), and are basi-cally two-dimensional. Therefore, one is forced to make a number of conceptual interpretations on the location and nature of the turbidite system in the subcrop between these two flanks. Other workers have contrasting views on this turbidite system. The aim here is not to claim a perfect model of the studied outcrops (which is impossible due to their nature), but to show how new technol-ogy can close the gap between the fully digital work flow of the oil and gas industry and the usage of the most perfect, detailed, and “true” data we have at hand — the outcrops.

The study area was chosen as a testing ground for a feasibility study for these techniques. An ideal dataset would involve outcrops with a more three-dimensional nature (an area with many ravines, etc.) in combination with other data types, such as boreholes, well logs, and shallow seismic or ground-penetrating radar (GPR). More perfect three-dimensional models can be made based on such data-sets. We have started to collect and work with such data and some of the potential for these datasets is shown later in this paper.

In short, our aim is to show how new techniques can revitalize the use of outcrop data in the oil and gas industry. While these techniques cannot replace traditional data collection, they demonstrate how 3-D data can be preserved in a workflow all the way from outcrop to model.

Methods for Collecting Spatially Accurate, Digital Outcrop Data

1 — GPS MappingThe satellite-based Global Positioning System (GPS) is the core of

digital outcrop studies. The system allows us to accurately calculate a global position in 3-D. In terms of geological field work, Real Time Kinematic (RTK) surveying is preferred. Here, observations made by a stationary receiver (base) are combined with those made with a roving receiver (rover). This surveying technique makes it possible to determine the position of the rover in real time with approximately 2 cm (1 in.) accu-racy. The most straightforward way of 3-D geological mapping is simply to walk out surfaces using RTK surveying. This method is highly accurate both in terms of surveying (more than 2 cm [1 in.] in practice due to the flexible human body) and in terms of data distribution (no surface, no data). However, the method requires accessible outcrops, is slow (limited by how fast one can walk on the outcrops), and any reinterpretation requires remapping. Approximate equipment cost: $40,000 US.

2 — Laser Rangefinder MappingLaser rangefinders can very accurately measure the distances to a given

target. When combined with a compass and inclinometer, the relative 3-D position of points on an outcrop can be measured. When combined with GPS, the global position of any point on the outcrop can be measured. Thus, bed boundaries or fault surfaces can be surveyed from a distance. This method is relatively fast and can also be used on inaccessible outcrops. How-ever, the accuracy is lower than with GPS Mapping (due mainly to the human eye, but also the laser tools), surfaces must be visible from a distance, the method encourages interpolation/extrapolation, and any reinterpreta-tion requires remapping. The cost of a laser tool is variable, but is typically approximately $4000 US. (an RTK GPS system is also usually required).

3 — Point Cloud Method (LIDAR Scanning)LIDAR (Light Detection And Ranging) can, just like laser rangefind-

ers, very accurately measure distances. Moreover, they can measure several thousand points per second, so they can effectively scan the topography of the outcrop (~0.1 km2 [~1 million ft2] per hour). Some instruments also have an attached digital camera so that each point can be colored. The result is a 3-D virtual outcrop that can be displayed on a computer (globally positioned if combined with GPS). There are many advantages to using this technique. The whole outcrop is essentially brought back to the office. Furthermore, the outcrops can be rotated and viewed from any perspective. Faults and surfaces can be digitized directly. However, a significant problem is related to the dense point cloud generated. Large outcrops can be difficult to render and work with, and integration with other data using different software has so far been almost impossible. Approximate cost: $80,000 US.

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4 — Photorealistic MethodWe prefer a method that has all the advantages of the point cloud method, but at the same time does not have the practical limita-

tions related to the extraordinarily large datasets. This method is here referred to as the “photorealistic method.” Like the point cloud method, the first step is to combine GPS and laser scanners to produce a globally positioned topographic model of the outcrop. The textural information of the point cloud data is not important in this method, so the scans need only to be dense enough to represent the 3-D geometry of the outcrop surface accurately. That means that the initial scan can be less dense than for the point cloud method, or that points can be reduced at a later stage (i.e., removing points where the surface is flat). A triangulated surface is then generated from the outcrop scan. This is an accurately positioned 3-D geometrical surface with no textural information.

Digital photographs are then taken, covering the whole scanned area, and like the scans, they should be taken from several angles to the outcrops. Finally, these photographs are accurately draped to the outcrop surface. Since the outcrops usually are very irregular and vertical, this is not straightforward. A linear interpolation from a few known points (i.e., the standard procedure for mapping aerial and satellite images onto elevation models) is too inaccurate to be used on outcrops. Instead, explicit texturization must be used, meaning that the exact pixel corresponding to each individual point on the 3-D outcrop surface needs to be determined. CyberMappingTM is a software that can do this, by using camera information and six known common points on both the geometrical surface and digital photograph (either by natural-feature recognition on scans and photographs or by placing reflectors on the outcrop prior to photograph-ing). The position and orientation of the camera when the photograph was shot can then be calculated, making explicit texturization possible. The result is an accurate 3-D digital photorealistic outcrop model with stunning verisimilitude that renders quickly even on small laptop computers (Xu et al., 2000).

The photorealistic method is fast (as fast as the point cloud method). Outcrops are digital so features such as surfaces and faults can be digitized at will in the office. Furthermore, the outcrops can be rotated and viewed from any angle, and quantitative data such as individual bed thickness can be measured directly. The method is accurate (sub-dm [0.3 ft] pixel error) and provides images with data so that interpretations are testable. The data renders quickly and can easily be integrated with other data (e.g., seismic data, GPR data, borehole data, and reservoir models) in a variety of software. The only known disadvantages are the equipment costs (same as point cloud method plus digital camera and software) and processing of data (typically a 1 km2 [.4 m2]) outcrop requires a few days of processing, increasing with outcrop complexity).

From Visualization to UtilizationPhotorealistic outcrop data are excellent for visualization purposes. One can easily introduce people to outcrops and demonstrate

many geological principles, making them ideal data for teaching purposes. Further one can rotate and look at outcrops from angles not possible in the field and zoom in and access otherwise inaccessible areas. But the real strength of these data lies beyond visualization. Some of the different applications are illustrated on this page and are further exemplified in a field case from the Ainsa Turbidite System on the next two pages.

Example 1 — Measuring a fold axis

To the left is a conventional photograph of Carboniferous slope sediments at “Point of No Relief,” Western Ireland. The sea cliffs are largely inaccessible. To the right is a photorealistic model of the same outcrop, but rotated to get a perspective view that is impossible in the field. We can now zoom in and rotate even more so that we can see down the axis of a slump fold (inset). This fold axis can now be measured accurately on the digital dataset.

Example 2 — Paleocurrent measurements

The pictures above are workstation screenshots from a photorealistic model of Carboniferous deltaic sediments at Tullig Point, Western Ireland. The inset shows a portion of the cliff, zoomed and rotated to give a good view of some cross-stratified sandstones. The direction of these structures can be measured directly and thus data from otherwise inaccessible outcrops can be added to conventional field data to improve the depositional model.


Example 3 — Digitizing surfaces The picture to the left is a photorealistic

model of the Austin Chalk, Texas, USA. The fact that this model is fully quantitative and that each pixel is positioned in space with sub-dm (0.3 ft) accuracy means that we can treat these data in the same way as seismic data. For example, we can digitize (or interpret) surfaces. The picture shows digitized points along a fault surface (red) and along a bedding surface (blue). This model also contains an outcrop from a parallel road cut, meaning we can digitize the corresponding fault and bed at a different location. Thus, we can generate 3-D fault and bedding surfaces from the digitized points.

Field case — Modeling the Ainsa II Turbidite System (TS) Using Photorealistic Outcrops

One of the big advantages of digital outcrop data, especially for the exploration and production (E&P) industry, is that they — in combination with traditional field data and other globally positioned data — can be used to generate sophisticated, static 3-D analog reservoir models. These models are positioned in the same coordinate system as the digital outcrop data, meaning that the real rocks (e.g., the photorealistic outcrop models) can be displayed synchronously with the modeled reality. We present a field study that shows how this can be done and illustrates, step-by-step, how these models can be built.

Field AreaThe study area is located in the South-Central Pyrenees, near the town of Ainsa, Spain. The Eocene basin fill comprises a prograding

alluvial to basin-floor tract. The studied slope section consists of several tectonically controlled sandy turbidite systems within otherwise mud-dominated deposits. One of these turbidite systems — the Ainsa II Turbidite System (TS) — is the focus of this study.

Step 1 — Collect existing dataAs with any other study, the first step is to collect any available relevant data. In addition to previously collected traditional outcrop

data and published data, digital elevation models (DEMs), satellite imagery, and aerial photos were acquired. The spatial data (satellite images on the left and aerial imagery and a geological map provided by J. A. Munoz and P. Arbues, University of Barcelona on the right) are registered and mapped onto the DEMs.

Step 2A — Collect digital field dataOur goal was to create accurate outcrop models of the Ainsa II TS. Therefore, we scanned

and photographed whole outcrop localities from different angles (some photographs were shot from the air to get an optimal perspective). We collected data covering about 150,000 m2 (~1,600,000 ft2) of outcrop. This took us about one week, but with the new faster scanners available today, it would have taken 3–4 days. Three to four persons are needed to do the job effectively due to all the equipment that needs to be carried around (such as GPS receivers, laser scanners, and batteries).

Step 2B — Collect traditional field dataAt the same time as the digital data is collected, one should collect traditional outcrop data

such as measured sedimentological sections, etc. The sections should be positioned, or traced, with the roving GPS receiver. It is always advantageous to know the field area beforehand. That way, one can determine, for instance, the different facies one wants to describe (and include in the model). An effective way to collect data for flow modeling purposes is to walk several sections with the GPS receiver and then survey each defined facies change (rather than to survey each bed on the outcrop). Strike and dip measurements, paleocurrent measure-ments, facies trends and proportions, and fault and fold orientations should all be noted. Some of these data can be acquired from the photorealistic models later, but again it is important to stress that the digital outcrop data should be a supplement (not a substitute) to the traditional outcrop data.

Step 3 — Process and merge different dataWhen field work is finished, the scans may need to be reduced in size and triangulated. Further, the photographs need to be draped

onto the triangulated surfaces, the different data (such as DEMs and photorealistic models) likely need to be transferred to a common coordinate system (i.e., datum and projection), and the different file formats may need to be transferred to formats required by the dif-ferent software that is to be used for further modeling and visualization. In our case, this part took about two months to carry out, but once done, an automated and much faster workflow can be established.



Using Outcrop Data in the 21st Century19

Step 7 — QC and visualize reality and model synchronouslyWhen the model is built one should integrate all data and visualize everything together. Facies and surfaces in the static reservoir

model should match those in the photorealistic models and DEMs (or else be modified). Approximately two months were required to do the modeling, integration, and synchronous visualization. Below are some figures showing synchronous visualization of the ground truth (the digital rocks) and the modeled rocks in a 3-D visualization facility. We have found such facilities ideal for discussion of geol-ogy and modeling issues across disciplines.

DiscussionAs a result of the digital 3-D outcrop data,

we were able to make a much more accurate and sophisticated reservoir model than if we were to follow a more conventional approach. The outcrop data are now much more valuable than conventional (qualitative and 2-D) data. Furthermore, petrophysical data from analo-gous fields can be added to investigate flow behavior of the reservoir. Also, smaller-scale models can be built to be used in upscaling studies. In short, the digital outcrop data and corresponding reservoir model is a much more valuable analog dataset for the E&P industry than traditional outcrop data. But the data can be even more valuable when integrated with behind-outcrop data such as GPR or shallow seismic, core, and log data. We are currently working on such datasets (see picture above right showing an integrated dataset from the Ferron Sandstone, Utah, USA, displayed in a 3-D visualization facility).

Step 4 — Draw conceptual modelsPrior to the actual quantitative modeling, one should draw conceptual geological models, to aid and steer the quantitative modeling.

Conceptual models can be generalized sections and maps of key surfaces, facies, facies trends, fault patterns, etc.

Step 5 — Digitize surfaces and create logsThe measured sections surveyed

by GPS can be transferred into a format manageable for the reservoir modeling software. Older sections that are traced on photographs can be digitized on the photorealistic models. We used both procedures to create sections with zone and facies logs. Then key stratigraphic surfaces and structural surfaces were digitized on the photorealistic dataset. We recognized several key erosive surfaces in the Ainsa II TS that separate the deposits into channel units, also deemed to be the flow units. Two of these are illustrated below.

The Buil Syncline structural surface was provided by J. A. Munoz, University of Barcelona (see Fernandez et al., 2004). Key stratigraphic surfaces were digitized around the outcrop belt of the syncline using all photorealistic models and the 25 m (82 ft) resolution DEM draped with geological map and aerial photographs. We digitized points in a 3-D visualization facility (CAVETM), but it can also now be done on a PC. Points for each surface were saved as an (x,y,z) dataset. Integration of points over distance and across the different datasets was trivial since all data was referenced by GPS and related to a common coordinate system.

Step 6 — Build a static reservoir modelWe used Irap RMS™ to create the reservoir model. The digitized points and logs were used to create the key surfaces. Top and

base reservoir surfaces were made parallel to the structural surface in the subcrop area, and the conceptual model was used to guide the other surfaces in the subcrop area. In theory, we could have fully mapped out each facies boundary in the field and thus built a deter-ministic facies model. Instead we used a number of measured sections (transferred into facies logs) and used a stochastic approach to fill the volume between the sections with facies. The conceptual models (based on trends, proportions, and shapes) were used to guide the model.


AcknowledgementsWe thank Hydro for permission to publish this work. We also thank M. Marzo, J. A. Munoz, and P. Arbues (University of Barcelona)

for providing us with some outcrop data through their Ainsa Consortium. D. Hodgetts (Basin and Stratigraphic Studies, Manchester University) kindly provided the LIDAR point cloud picture (p. 16, this paper). Finally, we thank the Atlas editors for constructive com-ments.

ReferencesFernandez, O., J. A. Muñoz, P. Arbués, O. Falivene, and M. Marzo, 2004, Three-dimensional reconstruction of geological surfaces: An

example of growth strata and turbidite systems from the Ainsa Basin (Pyrenees, Spain): AAPG Bulletin, v. 88, p. 1049–1068. Xu, X., C. L. V. Aiken, J. P. Bhattacharya, R. M. Corbeanu, K. C. Nielsen, G. A. McMechan, and M. G. Abdelsalam, 2000, Creating

virtual 3-D outcrop: The Leading Edge, v. 19, p. 199–202.

Suggested Further ReadingHodgetts, D., et al., 2004, Three-dimensional geological models from outcrop data using digital data collection techniques: An

example from the Tanqua Karoo depocenter, South Africa in A. Curtis and R. Wood, eds., Geological prior information: Informing science and engineering: Geological Society Special Publication 239, p. 57–75.

Xu, X., 2000, Three-dimensional virtual geology: Photorealistic outcrops and their acquisition, visualization, and analysis: Ph.D. Dissertation, University of Texas at Dallas, Dallas, Texas, USA, 169 p.

ConclusionsSpatially accurate, quantitative 3-D outcrop data can be collected using a range of different tools and methods. Such data have

wide applications in both academia and industry, some of which have been illustrated here. One key application lies in reservoir characterization studies.

We have shown that analog reservoir models can be built directly and accurately from digital outcrop data. It is likely that there will be multiple interpretations of a single outcrop, and therefore multiple reservoir analog model realizations, but by honoring all the available data from an outcrop in a spatially accurate way, one can eliminate any interpretations that simply cannot fit the available data in a reasonable way. Construction of reservoir analog models using these techniques allows both a spatially accurate and fully quantitative approach to outcrop data collection.



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