Identificación y delineación de intrusivos ígneos a partir de imágenes micro-resistivas de alta resolución de la pared del pozo e imágines profundas de la onda de corte horizontal obtenida con la herramienta acústica dipolar convencional en los yacimientos Volcán Auca Mahuida y las Manadas-Risco Alto en la Cuenca Neuquina, Argentina
Language: Español Description: 21 p. ; 25-46Subject(s): DDC classification:- 068.82 553.28 C62 2018 0015648
| Current library | Call number | Status | Barcode | |
|---|---|---|---|---|
| Biblioteca virtual | 068.82 553.28 C62 2018 0015648 (Browse shelf(Opens below)) | Not for loan | 200064914 |
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In the Neuquén Province, Argentina, the use of high technologies like borehole wall multi-frequency micro-resistive imaging and cross-dipole acoustics to obtain deep images into the formation. The results have significant benefits to understanding igneous intrusive distribution that have negative effects on all reservoirs associated with the Auca Mahuida Volcano (Fields: Auca Mahuida Volcano, Las Manadas and Risco Alto Norte).On the surface, a thick basaltic layer limits seismic coverage, complicating the study of the intrusive network. The dykes found on the surface have been extrapolated to the subsoil trying to establish correlation with the subsurface analysis. But based on more detailed analysis, the surface intrusive distribution is different from the subsurface because subsoil dykes may have used pre-existing fractures to intrude the sedimentary rock.With the micro-resistive images, it is possible to determine accurate strike, dip of fractures, beds, micro-faults, and intrusive, and differentiate the type of intrusive (dyke or sill). Micro-resistive images have limited value for confidently extrapolating the distribution of intrusives beyond the borehole wall. The deep-shear-wave imaging (DSWI), however, becomes relevant because this technology can map the intrusive bodies, and fracture corridors up to 30 meters inside the formation.Since the intrusive wants to be avoided when drilling future wells, it is concluded that the use of these two technologies in combination improves the understanding and prediction of their behavior
