INVESTIGADORES
BILMES Andres
congresos y reuniones científicas
Título:
ANÁLOGOS DE RESERVORIO Y VIAJES DE CAMPO MEDIANTE AFLORAMIENTOS VIRTUALES INTEGRADOS: DESAFÍOS Y PERSPECTIVAS FUTURAS
Autor/es:
BILMES, ANDRÉS; D'ELIA, LEANDRO; LOPEZ RAMIRO; MONTAGNA ALDO; VARELA, AUGUSTO; HODGETTS DAVID
Lugar:
Mendoza
Reunión:
Congreso; 11º Congreso de Exploración y Desarrollo de Hidrocarburos; 2022
Institución organizadora:
IAPG
Resumen:
The use of virtual outcrops in the oil industry is growing fast. These models, also known as DigitalOutcrop Models (DOMs) or Virtual Outcrop Models (VOMs) are 3D photorealistic digital models ofan outcrop or ground surface. Data for those models is mostly obtained using a photogrammetricmethod (i.e., Structure from Motion–Multi-View Stereo) with unmanned aerial vehicles (UAVs)combined with Differential GPS. This approach is beginning to be applied not only for educationalaspects such as virtual field trips, but also to enhance traditional field techniques to determinequantitative parameters of natural systems, improving the modelling of geological reservoirs. Oneof the main advantages of virtual outcrops is that they allow us to obtain quantitative geologicaldata of subseismic resolution (e.g., geometry and connectivity of geobodies, type and scale ofheterogeneities, and patterns, intensity and degree of connectivity of fracture networks) in a similarway to traditional methods usually applied in the field, (e.g., geological mapping, sedimentologicallog, dip-azimuth and dip measurements, scanlines, among others). This allows geoscientists tobetter understand and model the detailed subsurface reservoir architecture in order to predict fluidstorage and transmissivity. Based on different experiences performed with Virtual Outcrop data,this paper aims to show the potential and limitations of Virtual Outcrops in the petroleum industry,both for professional training (virtual field trips) and as hydrocarbon reservoir analogues. Theresults show that virtual outcrops are a tool with huge potential for geological understanding andstatistically reliable data collection when it is coupled together with traditional field observations.