System and method for property estimation from seismic data
A method is described for property estimation including receiving a seismic dataset representative of a subsurface volume of interest and a well log from a well location within the subsurface volume of interest; identifying seismic traces in the seismic dataset that correspond to the well location to obtain a subset of seismic traces; windowing the subset of seismic traces and the well log to generate windowed seismic traces and a windowed well log; multiplying the windowed seismic traces and the windowed well log by a random matrix to generate a plurality of training datasets; and training a neural network using the plurality of training datasets. The method may be executed by a computer system.
1. A computer-implemented method, comprising:
a. receiving, at a computer processor, a seismic dataset representative of a subsurface volume of interest and a well log r from a well location within the subsurface volume of interest;
b. identifying seismic traces in the seismic dataset that correspond to the well location to obtain a subset of seismic traces d;
c. windowing the subset of seismic traces and the well log to generate windowed seismic traces and a windowed well log;
d. multiplying the windowed seismic traces and the windowed well log by a random matrix to generate a plurality of training datasets {{tilde over (r)}, {tilde over (d)}}; and
e. training a neural network using the plurality of training datasets {{tilde over (r)}, {tilde over (d)}}, wherein the neural network may be trained using {tilde over (d)} as input to generate rock properties as output or using {tilde over (r)} as input to generate a seismic wavelet as output.
2. The method of claim 1 further comprising using the trained neural network to estimate rock properties from another seismic dataset.
3. The method of claim 2 further comprising using the rock properties for seismic imaging and interpretation.
4. The method of claim 1 wherein the well log is a reflectivity log.
5. The method of claim 1 further comprising using the trained neural network to estimate a wavelet from another well log.
6. A computer system, comprising:
one or more processors;
memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions that when executed by the one or more processors cause the system to execute:
a. receiving, at the one or more processors, a seismic dataset representative of a subsurface volume of interest and a well log r from a well location within the subsurface volume of interest;
b. identifying seismic traces in the seismic dataset that correspond to the well location to obtain a subset of seismic traces d;
c. windowing the subset of seismic traces and the well log to generate windowed seismic traces and a windowed well log;
d. multiplying the windowed seismic traces and the windowed well log by a random matrix to generate a plurality of training datasets {{tilde over (r)}, {tilde over (d)}}; and
e. training a neural network using the plurality of training datasets {{tilde over (r)}, {tilde over (d)}}, wherein the neural network may be trained using {tilde over (d)} as input to generate rock properties as output or using {tilde over (r)} as input to generate a seismic wavelet as output.
7. The computer system of claim 6 further comprising using the trained neural network to estimate rock properties from another seismic dataset.
8. The computer system of claim 7 further comprising using the rock properties for seismic imaging and interpretation.
9. The computer system of claim 6 wherein the well log is a reflectivity log.
10. The computer system of claim 6 further comprising using the trained neural network to estimate a wavelet from another well log.
11. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and memory, cause the device to execute:
a. receiving, at the one or more processors, a seismic dataset representative of a subsurface volume of interest and a well log r from a well location within the subsurface volume of interest;
b. identifying seismic traces in the seismic dataset that correspond to the well location to obtain a subset of seismic traces d;
c. windowing the subset of seismic traces and the well log to generate windowed seismic traces and a windowed well log;
d. multiplying the windowed seismic traces and the windowed well log by a random matrix to generate a plurality of training datasets {{tilde over (r)}, {tilde over (d)}}; and
e. training a neural network using the plurality of training datasets {{tilde over (r)}, {tilde over (d)}}, wherein the neural network may be trained using {tilde over (d)} as input to generate rock properties as output or using {tilde over (r)} as input to generate a seismic wavelet as output.