US 2024/0192394 A1 · MACHINE LEARNING-BASED TWO-STEP IMPEDANCE INVERSION METHOD AND APPARATUS USING SEISMIC DATA

Abstract

Techniques for a machine learning-based two-step impedance inversion method using seismic data are disclosed. In some embodiments of the disclosed technology, an impedance inversion method includes generating a domain adaptation model configured to predict, based on source data associated with a source area that includes a well, a P-impedance value of a target area that does not include a well, and generating, using the P-impedance value generated by the domain adaptation model, a P-impedance low frequency model configured to predict a final P-impedance value of the target area by performing an inversion. In this way, it is possible to accurately predict P-impedance value of an area where a well does not exist.

Claims15

Dependent claims

A machine learning-based two-step impedance inversion method using seismic data, comprising:

generating a domain adaptation model based on a source data associated with a source area that includes a well and a target area that does not include a well, and predicting a P-impedance value of the target area;
and
generating, using the P-impedance value generated by the domain adaptation model, a P-impedance low frequency model configured to predict a final P-impedance value of the target area by performing an inversion.

A machine learning-based two-step impedance inversion apparatus using seismic data, comprising:

a processor;
and
a storage unit communicatively connected to the processor, and configured to store program codes operating in the processor,
the program codes comprising:
a training module configured to generate a domain adaptation model for predicting a P-impedance value of a target area that does not include a well by using source data obtained from a source area that includes a well and target data obtained from the target area;
a preprocessing module configured to generate a P-impedance low frequency model by using the P-impedance value generated by the domain adaptation model;
and
an inversion module configured to generate a final P-impedance value by performing an inversion using the P-impedance low frequency model and the target data.

5 more elements

Dependencies are inferred from the claim text.

Machine learning-based two-step impedance inversion method and apparatus using seismic data

Life of this patent

Machine learning-based two-step impedance inversion method and apparatus using seismic data

Life of this patent

Abstract

Claims15