METHOD FOR PREDICTING A SEISMIC MODEL

1 . A method for determining a refined seismic model of a subterranean region, comprising:

obtaining an observed seismic dataset from the subterranean region;

obtaining a current seismic model of the subterranean region;

training, using a computer processor, a machine learning (ML) network using a plurality of seismic training models and a corresponding seismic training dataset for each seismic training model to produce a trained ML network;

predicting, using the trained ML network, a predicted seismic model from the observed seismic dataset;

determining, using the computer processor, a simulated seismic dataset from the current seismic model and a seismic wavelet using a forward modeling method;

determining, using the computer processor, a data penalty function based on a difference between the observed seismic dataset and the simulated seismic dataset;

determining, using the computer processor, a model penalty function based on a difference between the current seismic model and the predicted seismic model;

determining, using the computer processor, a composite penalty function based, at least in part, on a weighted sum of the data penalty function and the model penalty function; and

determining, using the computer processor, the refined seismic model based, at least in part, on finding an extremum of the composite penalty function.

2 . The method of claim 1 , further comprising:

determining, using the computer processor, an updated simulated seismic dataset from the refined seismic model and a refined seismic wavelet using the forward modeling method;

determining, using the computer processor, an updated data penalty function based on a difference between the observed seismic dataset and the refined simulated seismic dataset;

determining, using the computer processor, an updated model penalty function based on a difference between the refined seismic model and the predicted seismic model

determining, using the computer processor, an updated composite penalty function based, at least in part, on a weighted sum of the updated data penalty function and the updated model penalty function; and

determining, using the computer processor, an updated seismic model based, at least in part, on finding an extremum of the composite penalty function.

3 . The method of claim 1 , further comprising:

determining a location of a hydrocarbon reservoir based, at least in part, on the seismic model; and

planning, using a wellbore planning system, a wellbore path to intersect the hydrocarbon reservoir.

4 . The method of claim 1 , wherein training the ML network further comprises:

forming a composite training penalty function comprising:

a model penalty function measuring the difference between each seismic training model and a predicted seismic training model, predicted by the ML network from the corresponding seismic training dataset, and

a data penalty function based, at least in part, on a ML Jacobian operator; and

training the ML network based, at least in part, on finding an extremum of the composite training penalty function.

5 . The method of claim 1 , wherein training ML network further comprises:

augmenting the plurality of seismic training models and the corresponding seismic training datasets with the current seismic model and the simulated seismic dataset for the current seismic model; and

retraining the trained ML network based, at least in part, on the augmented plurality of seismic training models and the corresponding seismic training datasets.

6 . The method of claim 1 , wherein training the ML network further comprises:

forming a composite training penalty function comprising:

a model penalty function measuring the difference between each seismic training model and a predicted seismic training model, predicted by the ML network from the corresponding seismic training dataset, and

a data penalty function based, at least in part, on a deterministic Jacobian operator; and

training the ML network based, at least in part, on finding an extremum of the composite training penalty function.

7 . The method of claim 1 , wherein the extremum comprises a minimum.

8 . The method of claim 1 , wherein the forward modelling method comprises a physics-based forward modelling method.

9 . A non-transitory computer readable medium, storing instructions executable by a computer processor, the instructions comprising functionality for:

receiving an observed seismic dataset from a subterranean region;

receiving a current seismic model of the subterranean region;

training a machine learning (ML) network using a plurality of seismic training models and a corresponding seismic training dataset for each seismic training model to produce a trained ML network;

using the trained ML network to predict a predicted seismic model from the observed seismic dataset;

determining a simulated seismic dataset from the current seismic model and a seismic wavelet using a forward modeling method;

determining a data penalty function based on a difference between the observed seismic dataset and the simulated seismic dataset;

determining a model penalty function based on a difference between the current seismic model and the predicted seismic model;

determining a composite penalty function based, at least in part, on a weighted sum of the data penalty function and the model penalty function; and

determining a refined seismic model based, at least in part, on finding an extremum of the composite penalty function.

10 . The non-transitory computer readable medium of claim 9 , the instructions further comprising the functionality for:

determining a location of a hydrocarbon reservoir based, at least in part, on the refined seismic model; and

planning a wellbore path to intersect the hydrocarbon reservoir.

11 . The non-transitory computer readable medium of claim 9 , wherein training the ML network further comprises:

forming a composite training penalty function comprising:

a model penalty function measuring the difference between each seismic training model and a predicted seismic training model, predicted by the ML network from the corresponding seismic training dataset, and

a data penalty function based, at least in part, on a ML Jacobian operator; and

training the ML network based, at least in part, on finding an extremum of the composite training penalty function.

12 . The non-transitory computer readable medium of claim 9 , the instructions further comprising the functionality for:

augmenting the plurality of seismic training models and the corresponding seismic training datasets with the current seismic model and the simulated seismic dataset for the current seismic model; and

retraining the trained ML network based, at least in part, on the augmented plurality of seismic training models and the corresponding seismic training datasets.

13 . The non-transitory computer readable medium of claim 9 , wherein training the ML network further comprises:

forming a composite training penalty function comprising:

a model penalty function measuring the difference between each seismic training model and a predicted seismic training model, predicted by the ML network from the corresponding seismic training dataset, and

a data penalty function based, at least in part, on a deterministic Jacobian operator; and

training the ML network based, at least in part, on finding an extremum of the compo site training penalty function.

14 . The non-transitory computer readable medium of claim 9 , wherein the extremum comprises a minimum.

15 . The non-transitory computer readable medium of claim 9 , wherein the forward modelling method comprises a physics-based forward modelling method.

16 . A system, comprising:

a seismic acquisition system, configured to acquire an observed seismic dataset;

a computer system, configured to:

receive an observed seismic dataset from a subterranean region,

receive a current seismic model of the subterranean region,

train a machine learning (ML) network using a plurality of seismic training models and a corresponding seismic training dataset for each seismic training model to produce a trained ML network,

use the trained ML network to predict a predicted seismic model from the observed seismic dataset,

determine a simulated seismic dataset from the current seismic model and a seismic wavelet using a forward modeling method,

determine a data penalty function based on a difference between the observed seismic dataset and the simulated seismic dataset,

determine a model penalty function based on a difference between the current seismic model and the predicted seismic model,

determine a composite penalty function based, at least in part, on a weighted sum of the data penalty function and the model penalty function,

determine a refined seismic model based, at least in part, on finding an extremum of the composite penalty function, and

determine a location of a hydrocarbon reservoir based, at least in part, on the refined seismic model; and

a wellbore planning system configured to plan a planned wellbore path to intersect the location of the hydrocarbon reservoir.

17 . The system of claim 16 , further comprising a drilling system to drill a wellbore guided by the planned wellbore path.

18 . The system of claim 16 , wherein training the ML network further comprises:

forming a composite training penalty function comprising:

a model penalty function measuring the difference between each seismic training model and a predicted seismic training model, predicted by the ML network from the corresponding seismic training dataset, and

a data penalty function based, at least in part, on a ML Jacobian operator; and

training the ML network based, at least in part, on finding an extremum of the composite training penalty function.

19 . The system of claim 16 , wherein training the ML network further comprises:

forming a composite training penalty function comprising:

a model penalty function measuring the difference between each seismic training model and a predicted seismic training model, predicted by the ML network from the corresponding seismic training dataset, and

a data penalty function based, at least in part, on a deterministic Jacobian operator; and

training the ML network based, at least in part, on finding an extremum of the composite training penalty function.

20 . The system of claim 16 , wherein the computer system is further configured to:

augment the plurality of seismic training models and the corresponding seismic training datasets with the current seismic model and the simulated seismic dataset for the current seismic model; and

retrain the trained ML network based, at least in part, on the augmented plurality of seismic training models and the corresponding seismic training datasets.