Geophysical deep learning
AssigneeSchlumberger Technology Corporation
InventorsNader Salman, Victor Aarre, Hilde Grude Borgos, Michael Hermann Nickel
Life of this patent
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Abstract
A method can include selecting a type of geophysical data; selecting a type of algorithm; generating synthetic geophysical data based at least in part on the algorithm; training a deep learning framework based at least in part on the synthetic geophysical data to generate a trained deep learning framework; receiving acquired geophysical data for a geologic environment; implementing the trained deep learning framework to generate interpretation results for the acquired geophysical data; and outputting the interpretation results.
Claims20
A method comprising:
- selecting a type of geophysical data;
- selecting a type of algorithm;
- generating synthetic geophysical data of the selected type of geophysical data based at least in part on the algorithm;
- training a deep learning classifier framework based at least in part on the synthetic geophysical data to generate a trained deep learning classifier framework;
- receiving acquired geophysical data for a geologic environment;
- implementing the trained deep learning classifier framework to generate interpretation results for the acquired geophysical data;
- and
- outputting the interpretation results, wherein the interpretation results characterize structural features indicative of hydrocarbons in the geologic environment.
A system comprising:
- a processor;
- memory operatively coupled to the processor;
- and
- processor-executable instructions stored in the memory to instruct the system to:
- select a type of geophysical data;
- select a type of algorithm;
- generate synthetic geophysical data of the selected type of geophysical data based at least in part on the algorithm;
- train a deep learning classifier framework based at least in part on the synthetic geophysical data to generate a trained deep learning classifier framework;
- receive acquired geophysical data for a geologic environment;
- implement the trained deep learning classifier framework to generate interpretation results for the acquired geophysical data;
- and
- output the interpretation results, wherein the interpretation results characterize structural features indicative of hydrocarbons in the geologic environment.
One or more non-transitory computer-readable storage media comprising processor-executable instructions to instruct a computing system to:
- select a type of geophysical data;
- select a type of algorithm;
- generate synthetic geophysical data of the selected type of geophysical data based at least in part on the algorithm;
- train a deep learning classifier framework based at least in part on the synthetic geophysical data to generate a trained deep learning classifier framework;
- receive acquired geophysical data for a geologic environment;
- implement the trained deep learning classifier framework to generate interpretation results for the acquired geophysical data;
- and
- output the interpretation results, wherein the interpretation results characterize structural features indicative of hydrocarbons in the geologic environment.
Dependencies are inferred from the claim text.