Method for identifying subsurface fluids and/or lithologies

1. A method for identifying at least one of a subsurface pore-filling fluid, lithology and combinations thereof, the method comprising the steps of:

providing a training set of geophysical data, the geophysical data selected from field-acquired geophysical data, simulated geophysical data and combinations thereof;

training a backpropagation-enabled process on the training set to identify, during training, the presence of at least one of a subsurface pore-filling fluid, lithology, and combinations thereof; and

using the trained process on a field-acquired data set that is not part of the training set to infer presence of at least one of a subsurface pore-filling fluid, lithology and combinations thereof.

2. The method of claim 1 , wherein the backpropagation-enabled process is a deep learning process.

3. The method of claim 1 , wherein the backpropagation-enabled process is a supervised process and the training set of geophysical data is labeled.

4. The method of claim 3 , wherein a label for the labeled geophysical data has the same dimension as the dimension of the at least one of the subsurface pore-filling fluid, lithology and combinations thereof for which the presence has been inferred.

5. The method of claim 3 , wherein the supervised backpropagation-enabled process is a classification process that is conducted voxel-wise, slice-wise or volume-wise.

6. The method of claim 1 , wherein the backpropagation-enabled process is an unsupervised process.

7. The method of claim 6 , wherein the unsupervised backpropagation-enabled process is selected from the group consisting of a variational autoencoder process, a generative adversarial network process, and combinations thereof.

8. The method of claim 6 , wherein the unsupervised backpropagation-enabled process is a clustering process that is conducted voxel-wise, slice-wise or volume-wise.

9. The method of claim 1 , wherein the backpropagation-enabled process is a semi-supervised process and a subset of the training set of geophysical data is labeled for the presence of the at least one of a subsurface pore-filling fluid, lithology, and combinations thereof.

10. The method of claim 9 , wherein the semi-supervised backpropagation-enabled process is a semi-supervised variational autoencoder process, a semi-supervised generative adversarial network process, and combinations thereof.

11. The method of claim 1 , wherein the training step further comprises validating and testing.

12. The method of claim 1 , wherein the field-acquired geophysical data and the simulated geophysical data comprise seismic response data.

13. The method of claim 12 , wherein the seismic response data is selected from data comprised of single offset, multiple offsets, single azimuth, multiple azimuths, and combinations thereof for all common midpoints of field-acquired seismic data, simulated seismic data and combinations thereof.

14. The method of claim 13 , wherein the geophysical data are measured in a time domain.

15. The method of claim 13 , wherein the geophysical data are measured in a depth domain.

16. The method of claim 1 , wherein the pore-filling fluid is selected from the group consisting of gas, oil, brine, condensate, and combinations thereof.

17. The method of claim 1 , wherein the geophysical data has a dimension in the range of from 1 to 6.

18. The method of claim 1 , wherein the lithology is selected from the group consisting of sand, shale, limestone, carbonates, volcanics and combinations thereof.

19. The method of claim 1 , wherein the backpropagation-enabled process is a segmentation process.

20. The method of claim 1 , wherein the multi-dimensional seismic data set comprises multiple attributes.

21. The method of claim 20 , wherein the multiple attributes comprise 3 color channels.