System and method for 3d seismic velocity estimation
AssigneeChevron U.S.A. Inc.
InventorEnning Wang
Life of this patent
Estimating the term…
Abstract
A method is described for estimating seismic velocity from seismic data by training a neural network using a subset of a seismic dataset and the velocity model; estimating a second velocity model using the neural network and a second subset of the seismic dataset; and displaying the second velocity model on a graphical user interface. The method may be executed by a computer system.
Claims16
A computer-implemented method, comprising:
- a. receiving, at a computer processor, a 5D seismic dataset representative of a subsurface volume of interest and a 3D velocity model;
- b. preparing a subset of the 5D seismic dataset and a subset of the 3D velocity model to generate a training dataset;
- c. training a model using the training dataset to generate a trained model;
- and
- d. storing the trained model in computer memory or a non-transitory computer readable storage medium.
A computer-implemented method, comprising:
- a. obtaining a trained model from computer memory or a non-transitory computer readable storage medium;
- b. receiving a 5D seismic dataset;
- c. estimating a predicted velocity model using the trained model and a subset of the 5D seismic dataset;
- and
- d. displaying the predicted velocity model on a graphical user interface.
A computer system, comprising:
- one or more processors;
- memory;
- and
- one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions that when executed by the one or more processors cause the system to:
- a. receive, at the one or more processors, a 5D seismic dataset representative of a subsurface volume of interest and a 3D velocity model;
- b. prepare a subset of the 5D seismic dataset and a subset of the 3D velocity model to generate a training dataset;
- c. train a model using the training dataset to generate a trained model;
- and
- d. store the trained model in the memory or a non-transitory computer readable storage medium.
A computer system, comprising:
- one or more processors;
- memory;
- and
- one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions that when executed by the one or more processors cause the system to:
- a. obtain a trained model from the memory or a non-transitory computer readable storage medium;
- b. receive a 5D seismic dataset;
- c. estimate a predicted velocity model using the trained model and a subset of the 5D seismic dataset;
- and
- d. display the predicted velocity model on a graphical user interface.
A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and memory, cause the device to:
- a. receive, at the one or more processors, a 5D seismic dataset representative of a subsurface volume of interest and a 3D velocity model;
- b. prepare a subset of the 5D seismic dataset and a subset of the 3D velocity model to generate a training dataset;
- c. train a model using the training dataset to generate a trained model;
- and
- d. store the trained model in the memory or a non-transitory computer readable storage medium.
A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and memory, cause the device to:
- a. obtain a trained model from the memory or a non-transitory computer readable storage medium;
- b. receive a 5D seismic dataset;
- c. estimate a predicted velocity model using the trained model and a subset of the 5D seismic dataset;
- and
- d. display the predicted velocity model on a graphical user interface.
Dependencies are inferred from the claim text.