Transfer learning for ml-assisted seismic interpretation
AssigneeSchlumberger Technology Corporation
InventorsAdam Niven Shumaker, Arvind Sharma
Life of this patent
Estimating the term…
Abstract
A method includes receiving field seismic data that represents a subsurface, identifying features in the field seismic data using a machine learning model that was trained using at least one first synthetic seismic data set that includes one or more features and one or more labels of the features, and at least one second synthetic seismic data set, the first and second synthetic seismic data sets both generated based on a geological model. Noise is injected into the second synthetic seismic data based on the geological model. The method also includes generating a model of the subsurface based at least in part on the features that were identified in the field seismic data using the machine learning model.
Claims20
A method, comprising:
- receiving field seismic data that represents a subsurface;
- identifying features in the field seismic data using a machine learning model that was trained using at least one first synthetic seismic data set that includes one or more features and one or more labels of the features, and at least one second synthetic seismic data set, the first and second synthetic seismic data sets both generated based on a geological model, wherein noise is injected into the second synthetic seismic data based on the geological model;
- and
- generating a model of the subsurface based at least in part on the features that were identified in the field seismic data using the machine learning model.
A computing system, comprising:
- one or more processors;
- and
- a memory including one or more non-transitory, computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
- receiving field seismic data that represents a subsurface;
- identifying features in the field seismic data using a machine learning model that was trained using at least one first synthetic seismic data set that includes one or more features and one or more labels of the features, and at least one second synthetic seismic data set, the first and second synthetic data sets both generated based on a geological model, wherein noise is injected into the second synthetic seismic data based on the geological model;
- and
- generating a model of the subsurface based at least in part on the features that were identified in the field seismic data using the machine learning model.
A non-transitory, computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising:
- receiving field seismic data that represents a subsurface;
- identifying features in the field seismic data using a machine learning model that was trained using at least one first synthetic seismic data set that includes one or more features and one or more labels of the features, and at least one second synthetic seismic data set, the first and second synthetic seismic data sets both generated based on a geological model, wherein noise is injected into the second synthetic seismic data based on the geological model;
- and
- generating a model of the subsurface based at least in part on the features that were identified in the field seismic data using the machine learning model.
Dependencies are inferred from the claim text.