Integrated Workflow For Building 3d Digital Outcrop Models Using Unmanned Aerial Vehicles - Drones: Field Case Thamama Group, Wadih Rahbah, UAE.

机译:用于建立3D数字露出模型的综合工作流程使用无人驾驶飞行器 - 无人机:野外案例泰马马集团,瓦迪拉哈,阿联酋。



The aim of this paper is to provide an insight on the usage of unmanned aerial vehicles (drones) for capturing 3D photorealistic images of geological outcrops which can be used as digital analogs during 3D geological modelling. Recognizing the potential of the use of drones in many other fields has opened the doors for an endless variety of applications in the geoscience world as well. Specifically, in geological mapping and 3-D modeling activities, the use of these technologies can be seen as a cost-effective method of quickly and accurately surveying, mapping and capturing surface geological phenomena and processes that were more difficult and expensive for been surveyed in the past. Combining aerial and ground photography acquisitions, the image of geological outcrops can be captured and modelled in 3D using photogrammetry techniques, generating Digital Elevation Models (DEM). With DEM the users can perform measurements in multiple azimuths and compare and overlay geological, geophysical and petrophysical data at different scales and obtain multi-point statistics (MPS) for MPS modelling or conventional spatial statistical information for variogram analysis. Jumping ahead from the past where data was used for only qualitative comparison through pictures, the outcrop data can be used directly as a cloud of points to be used as 3-D wireframes into modelling software for enhancing reservoir architecture procedures (designing vertical layering schemes on grids) capturing reservoir heterogeneities at different scales. The creation of digital outcrop models using drone technologies is a cost effective solution to introduce the advantage of the field observations into more accurate subsurface models. The advantage of incorporating outcrop information into the geological modelling workflow is the ability to understand, based on direct observations of the complexity of the geological drivers, which one plays the major control on the property distribution and flow processes in the subsurface.



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