Generating low frequency models for seismic waveform inversion in formation regions with limited control wells

1. A method of generating a low frequency model of a subterranean formation for performing a seismic waveform inversion, the method comprising:

receiving, at a processor from a first seismometer, seismic data for a first region of the subterranean formation, the seismic data representing a propagation of seismic waves within the first region;

receiving, at the processor from a well log instrument, well log data of one or more wells located at the first region;

determining, by the processor based on the received seismic data of the first region, one or more relative layer attributes of the first region, the one or more relative layer attributes representing one or more relative elastic properties of one or more formation layers of the first region;

determining, by the processor based on the one or more relative layer attributes of the first region, one or more first input values for a machine learning model;

determining, by the processor based on the received well log data, one or more second input values for the machine learning model;

generating, based on the one or more first input values and the one or more second input values, by executing the machine learning model by the processor, a relative low frequency model for the first region;

extrapolating, by executing the machine learning model by the processor, the first relative low frequency model to a second region of the subterranean formation, the second region being distinct from the first region;

updating, based on the extrapolating, by executing the machine learning model by the processor, the relative low frequency model to span the first region and the second region;

scaling, by the processor based on the well log data of the first region, the relative low frequency model;

generating, by the processor based on the scaling, a scaled low frequency model representing one or more scaled elastic properties of the one or more formation layers of the first region and one or more formation layers of the second region; and

performing, by the processor based on the scaled low frequency model, the seismic waveform inversion.

2. The method of claim 1 , wherein extrapolating the relative low frequency model to the second region comprises recursively extrapolating the relative low frequency model to one or more additional regions of the subterranean formation.

3. The method of claim 2 , wherein updating the relative low frequency model to span the first region and the second region comprises recursively updating, based on the recursive extrapolation, the relative low frequency model to span the first region, the second region, and the one or more additional regions.

4. The method of claim 1 , further comprising:

receiving, at the processor from a second seismometer, seismic data of the second region, the seismic data representing a propagation of seismic waves within the second region;

determining, by the processor based on the received seismic data of the second region, one or more relative layer attributes of the second region, the one or more relative layer attributes of the second region representing one or more relative elastic properties of the one or more formation layers of the second region; and

determining, by the processor based on the one or more relative layer attributes of the second region, one or more third input values for the machine learning model.

5. The method of claim 4 , wherein updating the relative low frequency model to span the first region and the second region comprises updating, based on the relative low frequency model and the one or more third input values, by executing the machine learning model by the processor, the relative low frequency model to span the first region and the second region.

6. The method of claim 5 , wherein the second region is void of wells.

7. The method of claim 5 , further comprising measuring, by the second seismometer, the seismic data of the second region.

8. The method of claim 7 , wherein the machine learning model is independent of well log data from the second region.

9. The method of claim 1 , wherein determining the one or more relative layer attributes of the first region comprises transforming, by the processor, the seismic data of the first region by a −90 degree phase shift prior to determining the one or more relative layer attributes.

10. The method of claim 1 , wherein the scaled low frequency model represents at least one of a density, a velocity, and an impedance for the one or more formation layers of the first region and the one or more formation layers of the second region.

11. The method of claim 1 , wherein updating the relative low frequency model to span the first region and the second region comprises updating the second relative low frequency model to span the first region and the second region in entirety.

12. The method of claim 1 , further comprising:

measuring, by the first seismometer, the seismic data at the first region; and

measuring, by the well log instrument, the well log data at the first region.

13. The method of claim 1 , further comprising:

determining, by the processor based on the seismic inversion, one or more well sites;

drilling, by a drill, one or more wellbores at each of the one or more well sites; and

extracting, by a pump, hydrocarbons from the one or more wellbores at the one or more well sites.

14. A system of generating a low frequency model of a subterranean formation for performing a seismic waveform inversion, the system comprising:

a first seismometer operable to measure seismic data of a first region of the subterranean formation, the seismic data representing a propagation of seismic waves within the first region;

a well log instrument operable to measure well log data of one or more wells located at the first region;

a computer storing computer instructions that, when executed by a processor of the computer, cause the processor to perform operations comprising:

receiving, from the first seismometer, the seismic data for the first region;

receiving, from the well log instrument, the well log data of the one or more wells located at the first region;

determining, based on the received seismic data of the first region, one or more relative layer attributes of the first region, the one or more relative layer attributes representing one or more relative elastic properties of one or more formation layers of the first region;

determining, based on the one or more relative layer attributes of the first region, one or more first input values for a machine learning model;

determining, based on the received well log data, one or more second input values for the machine learning model;

generating, based on the one or more first input values and the one or more second input values, by executing the machine learning model, a relative low frequency model for the first region;

extrapolating, by executing the machine learning model, the relative low frequency model to a second region of the subterranean formation, the second region being distinct from the first region;

updating, based on the extrapolating, by executing the machine learning model, the relative low frequency model to span the first region and the second region;

scaling, based on the well log data of the first region, the relative low frequency model;

generating, based on the scaling, a scaled low frequency model representing one or more scaled elastic properties of the one or more formation layers of the first region and one or more formation layers of the second region; and

performing, based on the scaled low frequency model, the seismic waveform inversion.

15. The system of claim 14 , further comprising a second seismometer operable to measure seismic data of the second region of the subterranean formation, the seismic data representing a propagation of seismic waves within the second region.

16. The system of claim 15 , wherein the computer instructions further include computer instructions that cause the processor to perform operations comprising:

receiving, from the second seismometer, the seismic data of the second region;

determining, based on the received seismic data of the second region, one or more relative layer attributes of the second region, the one or more relative layer attributes of the second region representing one or more relative elastic properties of the one or more formation layers of the second region; and

determining, based on the one or more relative layer attributes of the second region, one or more third input values for the machine learning model.

17. The system of claim 16 , wherein updating the relative low frequency model to span the first region and the second region comprises updating, based on the relative low frequency model and the one or more third input values, by executing the machine learning model, the relative low frequency model to span the first region and the second region in entirety.

18. The system of claim 16 , wherein the second region is void of wells.

19. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

receiving, from a first seismometer, seismic data for a first region of a subterranean formation, seismic data representing a propagation of seismic waves within the first region;

receiving, from a well log instrument, well log data of one or more wells located at the first region;

determining, based on the received seismic data of the first region, one or more relative layer attributes of the first region, the one or more relative layer attributes representing one or more relative elastic properties of one or more formation layers of the first region;

determining, based on the one or more relative layer attributes of the first region, one or more first input values for a machine learning model;

determining, based on the received well log data, one or more second input values for the machine learning model;

generating, based on the one or more first input values and the one or more second input values, by executing the machine learning model, a relative low frequency model for the first region;

extrapolating, by executing the machine learning model, the relative low frequency model to a second region of the subterranean formation, the second region being distinct from the first region;

updating, based on the extrapolating, by executing the machine learning model, the relative low frequency model to span the first region and the second region;

scaling, based on the well log data of the first region, the relative low frequency model;

generating, based on the scaling, a scaled low frequency model representing one or more scaled elastic properties of the one or more formation layers of the first region and one or more formation layers of the second region; and

performing, based on the scaled low frequency model, seismic waveform inversion.

20. The non-transitory computer-readable medium of claim 19 , wherein the computer instructions further include computer instructions that cause the at least one processor to perform operations comprising:

receiving, from a second seismometer, seismic data of the second region, the seismic data representing a propagation of seismic waves within the second region;

determining, based on the received seismic data of the second region, one or more relative layer attributes of the second region, the one or more relative layer attributes of the second region representing one or more relative elastic properties of the one or more formation layers of the second region; and

determining, based on the one or more relative layer attributes of the second region, one or more third input values for the machine learning model,

wherein updating the second relative low frequency model to span the first region and the second region comprises updating, based on the relative low frequency model and the one or more third input values, by executing the machine learning model, the second relative low frequency model to span the first region and the second region in entirety.