期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 18, 期 11, 页码 2025-2029出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.3012479
关键词
Computational modeling; Oils; Uncertainty; Reservoirs; Brain modeling; Minerals; Context modeling; Cross-validation; kriging; sequential Gaussian simulation (SGS); turbidite lobes; variogram
类别
资金
- Petrobras [10.13039/501100004225-PETROBRAS]
Turbidite deposits are potential oil reservoirs, and the detection methods usually involve sound wave emission. However, uncertainties in the data have prompted scientists to develop a geostatistics-based method to quantify this uncertainty.
Turbidite deposits are known to be potential oil reservoirs. The techniques used to detect these deposits are usually indirect measurement methods, normally, using sound waves emission. From several studies, it is known that these data have uncertainties. With the development of new technologies and the relevance of the oil exploration area, this theme has been gaining importance. However, this issue remains a major challenge for the scientific community. This work aims to develop a method based on geostatistics to generate possible scenarios (ensembles) that allow to quantify the uncertainties of the data used to identify turbidite deposits. For this, a set of coordinates extracted from the F3 field was used as study area. The results obtained showed that the methodology proposed in this study is appropriate to quantify the uncertainties in the detection of turbidite deposits.
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