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US 2024/0404650 A1Published application
Generative machine learning model assisted statistical inversion for borehole sensing
AssigneeHalliburton Energy Services, Inc.
InventorsXusong WANG, Christopher Michael JONES, Xiang WU, Jichun SUN
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Abstract
Aspects of the subject technology relate to systems, methods, and computer readable media for applying generative machine learning for performing statistical inversion in borehole sensing. A method can comprise implementing an inversion workflow for borehole sensing. The method can also comprise applying a generative machine learning network technique to sample candidate material variables as part of the inversion workflow.
Claims20
A method of mapping a first parameter to a second parameter, the method comprising:
- training a first machine learning (ML) model comprising a first hidden layer to accurately return a reconstructed first parameter as a first output when provided with the first parameter as a first input, thereby producing a trained first hidden layer;
- training a second ML model comprising a second hidden layer to accurately return a reconstructed second parameter as a second output when provided with the second parameter as a second input, thereby producing a trained second hidden layer;
- training a third ML model comprising a linking hidden layer to accurately return the trained second hidden layer as a third output when provided with the trained first hidden layer as a third input, thereby producing a trained linking hidden layer;
- constructing a ML system comprising a first portion of the first ML model, a second portion of the second ML model, and the third ML model, with the first hidden layer connected to the third input and the third output connected to the second hidden layer, such that providing a first plurality of values of the first parameter as a system input produces a second plurality of values of the second parameter as a system output.
1 more element
A system comprising:
- a processor configured to accept a signal;
- and
- a computer-readable memory coupled to the processor, the memory comprising a machine learning (ML) system that comprises:
- a first machine learning (ML) model comprising a first input, a first trained hidden layer, and a first output;
- a second ML model comprising a second input, a second trained hidden layer, and a second output;
- and
- a third ML model comprising a third input coupled to the first hidden layer, a trained linking hidden layer, and a third output coupled to the second hidden layer;
- wherein:
- the first ML model is trained to return a reconstructed first parameter as the first output when provided with a true first parameter as the first input, thereby producing the trained first hidden layer;
- the second ML model is trained to accurately return a reconstructed second parameter as the second output when provided with a true second parameter as the second input, thereby producing the trained second hidden layer;
- the third ML model is trained to accurately return the trained second hidden layer as the third output when provided with the trained first hidden layer as the third input, thereby producing the trained linking hidden layer;
- and
- providing a first plurality of values of the first parameter as a system input produces a second plurality of values of the second parameter as a system output.
10 more elements
A computer-readable memory comprising a machine learning (ML) system that comprises:
- a first machine learning (ML) model comprising a first input, a first trained hidden layer, and a first output;
- a second ML model comprising a second input, a second trained hidden layer, and a second output;
- and
- a third ML model comprising a third input coupled to the first hidden layer, a trained linking hidden layer, and a third output coupled to the second hidden layer;
- wherein:
- the first ML model is trained to return a reconstructed first parameter as the first output when provided with a true first parameter as the first input, thereby producing the trained first hidden layer;
- the second ML model is trained to accurately return a reconstructed second parameter as the second output when provided with a true second parameter as the second input, thereby producing the trained second hidden layer;
- the third ML model is trained to accurately return the trained second hidden layer as the third output when provided with the trained first hidden layer as the third input, thereby producing the trained linking hidden layer;
- and
- providing a first plurality of values of the first parameter as a system input produces a second plurality of values of the second parameter as a system output.
7 more elements
Dependencies are inferred from the claim text.