Seismic denoising
AssigneeShearwater Geoservices Software Inc.
InventorsChengbo Li, Yu Zhang
Life of this patent
Estimating the term…
Abstract
Leveraging migration and demigration, here we propose a learning-based approach for fast denoising with applications to fast-track processing. The method is designed to directly work on raw data without separating each noise type and character. The automatic attenuation of noise is attained by performing migration/demigration guided sparse inversion. By discussing examples from a Permian Basin dataset with very challenging noise issues, we attest the feasibility of this learning-based approach as a fast turnaround alternative to conventional denoising methodology.
Claims24
A process for automatically suppressing noise in seismic data where the process comprises:
- obtaining raw data (d) from a seismic survey;
- estimating a reference image ({tilde over (m)}) using a seismic image processing technique selected from automatic gain control, migration and stacking;
- demigrating the data from the reference image ({tilde over (m)}) and building a dictionary from the demigrated data by unsupervised learning;
- solving a dual-domain inversion problem to simultaneously invert signal and noise to obtain a denoised copy of the raw data (d).
A process for imaging a subterranean reservoir comprising:
- selecting a survey area;
- designing a seismic survey to image a subterranean formations;
- acquiring raw data using seismic sources and receivers;
- obtaining raw data (d) from the seismic survey;
- estimating a stacked image ({tilde over (m)});
- demigrating the data from the stacked image ({tilde over (m)}) to attain a clean reference;
- building a dictionary from the demigrated data comprising;
- solving an unsupervised dictionary learning problem;
- solving a dual-domain inversion to leverage sparsity, compressibility or both to guide attenuating noise of the raw data (d);
- simultaneously inverting signal and noise to provide an improved signal wherein said improved signal has automatic noise reduction with amplitude preservation.
A process for producing hydrocarbons from a subterranean reservoir comprising:
- selecting a subterranean formation for hydrocarbon production;
- designing a seismic survey to image the subterranean formation;
- acquiring raw data using seismic sources and receivers;
- obtaining raw data (d) from a seismic survey;
- estimating a stacked image ({tilde over (m)}) with mitigated artifacts;
- demigrating the data from the stacked image ({tilde over (m)}) to attain a clean reference;
- building a reference from the demigrated data;
- solving an unsupervised dictionary learning problem;
- solving a dual-domain inversion to leverage sparsity, compressibility or both to guide attenuating noise of the raw data (d);
- simultaneously inverting signal and noise to provide an improved signal;
- comparing a direct demigration to the improved signal to obtain denoised data with a proper amplitude versus offset;
- and
- constructing a well in said subterranean reservoir to produce hydrocarbons.
Dependencies are inferred from the claim text.