Physics-constrained deep learning joint inversion
AssigneeSaudi Arabian Oil Company
InventorsDaniele Colombo, Weichang Li, Diego Rovetta
Life of this patent
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Abstract
A deep learning framework includes a first model for predicting one or more attributes of a system; a second model for predicting one or more attributes of the system; at least one coupling operator combining the first and second models; and at least one inversion module for receiving the combined first and second models from the coupling operator. The inversion module simultaneously optimizes the first model and the second model, thereby resulting in a composite objective function representative of a prediction that is outputted to at least one user.
Claims24
A deep learning framework comprising:
- a first model for predicting one or more attributes of a system;
- a second model for predicting one or more attributes of the system;
- at least one coupling operator combining the first and second models;
- and
- at least one inversion module for receiving the combined first and second models from the at least one coupling operator,
- wherein the at least one inversion module simultaneously optimizes the first model and the second model, thereby resulting in a composite objective function representative of a prediction that is outputted to at least one user.
A method of training a neural network, comprising:
- inputting data into a model;
- pre-processing the data;
- defining an input data structure;
- defining at least one output parameter around which the neural network is optimized;
- creating test and training data sets from data input into the model;
- training the model;
- and
- updating the model based at least partially on new data that is inputted into the model after the model has been trained.
Dependencies are inferred from the claim text.