Recent developments in workflows and techniques for the integration and analysis of terrestrial LiDAR (Light Detection And Ranging) and conventional outcrop datasets are demonstrated through three case studies. The first study shows the power of three-dimensional (3D) data visualization, in association with an innovative surface-modelling technique, for establishing large-scale 3D stratigraphical frameworks. The second presents an approach to derive reliable geometrical data on sediment-body geometries, whereas the third presents a new technique to quantify the proportions, distributions and variability of sedimentary facies directly from outcrop. In combination, these techniques provide essential conditioning data for geocellular and stochastic facies modelling. Built upon robust, reproducible and quantitative data, the resultant models combine realistic 3D geological architectures with sufficient quantities of reliable numerical data required for stable statistical analysis and establishing uncertainty. Together this new information provides detailed understanding and quantification of the 3D complexity of the sedimentary systems in question, thus offering insights of value for predicting the subsurface anatomy of analogous petroleum systems. As such, use of LiDAR, when combined with conventional field geology, offers a powerful tool for quantitative outcrop analysis, tightly constraining 3D structural and stratigraphical interpretations, and effectively increasing the statistical significance of outcrop analogues for reservoir characterization.
The application and benefits of employing digital outcrop models (DOMs) are discussed using two Triassic fluvial case studies to demonstrate data collection and integration methods. Developments in data analysis techniques are examined to demonstrate their utility for collecting meaningful and reliable statistical information needed to build realistic stochastic reservoir models. To establish a significant geostatistical dataset a large number of accurate observations are required. It is difficult to get the necessary statistics using subsurface data alone, due to the limited resolution and/or areal coverage of respectively seismic and well data. Outcrop studies are, therefore, commonly utilized to provide analogue statistical information (e.g. channel width, length, thickness and thickness vs. width ratio). Traditional data collection methods used in the field are however largely restricted to areas with (easy) physical access, or using remote observations with limited accuracy, such as photographic methods.Digital data collection techniques such as LiDAR (Light Detection and Ranging) and differential GPS allow more accurate measurements, as well as from previously inaccessible locations, to be taken of sedimentary architecture. The technique generates much larger volumes of measurements, as the area from which accurate data can be extracted is increased. This offers a more meaningful statistical dataset, hence reducing uncertainty in the final reservoir model.Both case studies, the Oukaimeden Sandstone Formation (OSF), Morocco and Wolfville Formation, Canada, are from Late Triassic braided fluvial systems. The OSF dataset has been used to illustrate how geometric information of channel width versus thickness relationships (W:T) are collected using a projection plane technique. The results show W:T variations between 3.49:1 in the Lower Oukaimeden member, 1.54:1 in the Middle Oukaimeden member and 3.75:1 in the Upper Oukaimeden member, demonstrating the observed architectural evolution of the fluvial system. The Wolfville Formation case study shows how DGPS in combination with LiDAR data has been used to more accurately map faults to obtain statistical information on fault orientation (NE-SW) and length (mean ¼ 38.3 m and median ¼ 18.2 m). Another applied analysis technique utilizes a facies classified point-cloud to aid surface correlations between sedimentary logs and construct a log based correlation panel from which estimates of facies frequencies are derived.
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