# Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network
## 6 Application on Field Data  
Applying deep learning to field data is a very challenging task due to two main reasons, which are (1) the residual noise presence in the seismic data and (2) the unavailability of the true label data. To test the robustness of our approach to field data, we used seismic data from a Malaysian field with the labels generated by [PERSON], [PERSON], et al. [42] via rock-physics-guided, deep-learning-based properties inversion (Figure 17). The same workflow was applied to the field data, such as normalization and transformation to 256 \(\times\) 256 patches. The total number of patches was 512, of which 90% of them were used for training and the other 10% for validation. We repeated the same procedure for training. The best model from the first training was re-trained with a smaller kernel size for the discriminator. The best model from this was then used for testing. The testing data (Figure 18) was a patch that was never used in the training and validation.  
Before examining the results in detail, let us look at the accuracy metric. Our objective here was to predict the value as close as possible to the real value. We then established an accuracy metric (Figure 19) that compared the value of each element with a 5% margin of error. We counted the number of correct predictions and divided them by the total number of elements to get our final accuracy number.  
Table 3 shows the results of the field data test. In general, MS-pix2 pix produced predictions of higher accuracy than the predictions coming from the pix2 pix network (Figure 20). MS-pix2 pix (Figure 21) managed to predict the velocity and density accurately. This would help in producing better-migrated images of the subsurface thus resulting in a better interpretation of the geological features of the area. The porosity prediction was also improved in MS-pix2 pix, which can lead to a better delineation of good reservoir rocks. However, the prediction of water saturation remains a challenge.  
Figure 16: Water saturation prediction results of Inline 1. (**a**): Prediction from pix2 pix. (**b**): Prediction from MS-pix2 pix. (**c**): The ground truth. MS-pix2 pix corrected the structure predicted using pix2 pix. However, it failed to detect the water saturation layer.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Properties** & **Pix2 pix** & **MS-pix2 pix** \\ \hline P-velocity & 91.1 & 97.1 \\ Porosity & 21.6 & 33.1 \\ Density & 97.3 & 99.8 \\ Water saturation & 0.04 & 17.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The prediction accuracy of the field data testing. MS-pix2 pix managed to improve the prediction of the Pix2 Pix network.  
Figure 17: Seismic and subsurface properties data used in field data testing. The subsurface properties were generated via the rock physics method. The actual generated data only contained 160 samples. As pix2 pix requires 256 samples, sample 161 to 256 was filled with sample 1 to 96.  
Figure 19: The accuracy metric. As we are interested in the right value at each location, we introduced element-wise comparison with a 5% margin of error. The values at the same location of the two images were compared. If the difference in value was within 5% of the true value, it was considered “correct”. The total number of “correct” predictions was then divided by the total number of elements to give us the final accuracy.  
Figure 18: The seismic section used as the input data for testing.  
Figure 20: The pix2 pix prediction of the field data test. The first row is the result for velocity, followed by the result for porosity in the second row, density in the third row, and water saturation in the last row. The first column shows the pix2 pix result, followed by the ground truth in the second column and the difference in the third column. It can be seen that the velocity, porosity, and density predictions are following the geological structure as the ground truth. The water saturation prediction is horizontal, cutting across the structure and far from the ground truth.