AAPG Annual Convention and Exhibition

Datapages, Inc.Print this page

Quantitative Geology From Digital Outcrop Data for the Characterization of Hydrocarbon Reservoirs

Abstract

Lidar mapping is rapidly becoming a regular addition to the field geologist's toolkit. Using lidar data it is now easy to make photorealistic models which produce visually pleasing models which are easy to interpret, however this is only the case if the features of interest are clearly visible in the digital photograph, which in rocks of fairly uniform colour may not always be the case. The high resolution nature of lidar derived digital outcrop models facilitates the generation of surface attributes (such as curvature, co-planarity and roughness for example) which may be used to highlight features not easily seen in the photo-realistic model. These surface attributes not only make manual interpretation of the data easier, but facilitate the development of more automated mapping and tracking tools, which are essential in the large datasets provided by terrestrial and airborne laser scanning. In-house software called VRGS has been developed to aid in the interpretation and analysis of lidar and other digital data, and allow traditional field data collection methodologies and data types to be integrated into the digital dataset. This integrated dataset then facilitates a whole new range of approaches which help analyse and interpret the data. Combining the surface attribute method with the photorealistic approach provides a digital outcrop model from which a very large amount of information can be derived. Cloud based high performance computing (HPC) facilitates the used of large datasets on portable hardware such as tablet computers, with the majority of the data processing being done remotely. This then provides the ability to use these large lidar datasets on affordable and portable hardware while in the field. The information extracted from these outcrop analogue datasets include geostatistics on the underlying geological control on the surface morphology such as bedding orientation, fault geometry and facies distribution, which may then be used to better model geological heterogeneity in subsurface hydrocarbon reservoirs of similar age and depositional environment to the outcrop analogue. Several examples of outcrop datasets will be presented, illustrating how an integrated approach to digital and more traditional data collection techniques leads to improved data quality, reduced uncertainty and improved time efficiency in the field.