1. Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning.
- Author
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Rincon, Katerine, Araújo, Ramon C. F., Galvão, Moisés M., Xavier-de-Souza, Samuel, de Araújo, João M., Barros, Tiago, and Corso, Gilberto
- Subjects
SEISMIC wave velocity ,OCEAN bottom ,SEISMIC networks ,MACHINE learning ,DATABASES - Abstract
In this study, we used machine learning (ML) to estimate time-lapse velocity variations in a reservoir region using seismic data. To accomplish this task, we needed an adequate training set that could map seismic data to velocity perturbation. We generated a synthetic seismic database by simulating reservoirs of varying velocities using a 2D velocity model typical of the Brazilian pre-salt ocean bottom node (OBN) acquisition, located in the Santos basin, Brazil. The largest velocity change in the injector well was around 3% of the empirical velocity model, which mimicked a realistic scenario. The acquisition geometry was formed by the geometry of 1 shot and 49 receivers. For each synthetic reservoir, the corresponding seismic data were obtained by estimating a one-shot forward-wave propagation using acoustic approximation. We studied the reservoir illumination to optimize the input data of the ML inversion. We split the set of synthetic reservoirs into two subsets: training (80%) and testing (20%) sets. We point out that the ML inversion was restricted to the reservoir zone, which means that it was inversion-oriented to a target. We obtained a good similarity between true and ML-inverted reservoir anomalies. The similarity diminished for a situation with non-repeatability noise. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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