# Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning
## 4 Conclusions  
The 4D seismic inversion encompasses a myriad of different techniques [1,32]. In this work, we present an option for methodological simplicity, instead of introducing rock physics, production features, or detailed reservoir characterization [11,33]. We employ seismic data produced from an acoustic wave and invert the reservoir velocity using just one shot and dozens of receivers. The results are satisfactory when we consider the low computational cost and the economical use of input seismic data. The computational time for training and testing the 5000 samples used in this paper was around 4 h at a center for high-performance computing at our university (NPAD/UFRN). In the same computing facility, a similar 4D inversion [20] took one hundred hours to perform a similar inversion using full-waveform inversion (FWI) methodology. The high computational cost of FWI is related to a substantial degree of memory usage, and the wave propagation is associated with one iteration in the optimization procedure that uses data from a hundred seismic shots. Moreover, we emphasize that in our inversion effort, we employed a single shot, which in part explains the comparative low computational cost. Indeed, a usual seismic inversion can use a hundred shots to provide good illumination to perform an acceptable inversion. We point out an important limitation of our methodology: the diversity and quality of the training dataset. The success of the ML inversion is strongly dependent on the quality of the training dataset. In fact, in a field case, if the actual velocity anomaly significantly differed from the anomalies presented in the training dataset, the inversion would not be successful. In this way, a successful application of our methodology in a field case would depend on a good simulation of the reservoir changes over time, which entails reservoir simulations with the production of ample scenarios. The present study comprises a simple target-oriented ML inversion methodology based on one-shot acquisition geometry and a novel neural network architecture. In future work, we intend to expand our methodology in several directions, for instance, to introduce more shots in the acquisition geometry for each survey to test the method with greater illumination in the reservoir region. Another open line of research is to improve the ML training with seismic data contaminated by non-repeatability noise. We stress that in our methodology, we initially suppose that we can erase the non-repeatability noise, but we are aware that in practical situations, this is impossible. In this way, we tested the methodology with a residual non-repeatability noise and the results are acceptable. To  
Figure 9: Spatial distribution of \(\Delta v\) prediction errors for the test scenarios with (**a**) perfect repeatability, (**b**) \(\pm 0.1\) m geometry 4D noise, and (**c**) \(\pm 0.5\) m geometry 4D noise. conclude, we present in this paper an ML method to perform a 4D seismic inversion that has low computational complexity when compared with traditional FWI inversion. Conceptualization, G.C. ; Methodology, R.C.F.A. ; Software, R.C.F.A., M.M.G. and T.B. ; Validation, S.X.-d.-S.; Formal analysis, M.M.G., T.B. and G.C. ; Investigation, K.R. and T.B. ; Resources, J.M.d.A. ; Data curation, J.M.d.A. ; Writing--original draft, G.C. ; Writing--review & editing, R.C.F.A. ; Visualization, K.R. ; Funding acquisition, S.X.-d.-S. and J.M.d.A. All authors have read and agreed to the published version of the manuscript. This research was funded by Shell Brazil through the \"New computationally scalable methodologies for target-oriented 4D seismic in pre-salt reservoirs\" R&D project. Additional funding was provided by the CNPq Brazilian research Agency, under grants 307907/2019-8 and 311589/2021-9. The modeled seismic data supporting the conclusions of this study will be made available by the authors on razonable request.