System and method for seismic inversion

Patent metadata

Field	Value
Publication number	US-11796705-B2
Application number	US-202117223364-A
Country	US
Kind code	B2
Filing date	Apr 6, 2021
Priority date	Apr 7, 2020
Publication date	Oct 24, 2023
Grant date	Oct 24, 2023

Abstract

A method is described for inverting seismic data including obtaining well logs representative of subsurface volumes of interest; generating an amplitude variation with angle (AVA) database from the well logs by seismic modeling, wherein the seismic modeling is performed a plurality of times for all combinations of fluid substitutions of brine, oil, and gas and low porosity, mid-porosity, and high porosity; generating a trained AVA model using the AVA database; obtaining a seismic dataset; calibrating the seismic dataset; computing seismic attributes for the calibrated seismic dataset using statistics for AVA classification; and generating direct hydrocarbon indicators as a function of position in the subsurface volume of interest by applying the trained AVA model to the seismic attributes. The method is executed by a computer system.

First claim

What is claimed is:

1. A computer-implemented method for training an amplitude variation with angle (AVA) model to generate direct hydrocarbon indicators as a function of position in a subsurface volume of interest, the method being implemented in a computer system that comprises a computer processor and non-transient storage medium, the method comprising:
a. obtaining, from the non-transient storage medium, well logs representative of subsurface volumes of interest;
b. generating, via the computer processor, an amplitude variation with angle (AVA) database from the well logs by seismic modeling, wherein the seismic modeling is performed a plurality of times for all combinations of fluid substitutions of brine, oil, and gas and low porosity, mid-porosity, and high porosity;
c. generating, via the computer processor, a trained AVA model using the AVA database;
d. storing the trained AVA model in the non-transient storage medium;
e. obtaining, from the non-transient storage medium, a seismic dataset representative of a particular subsurface volume of interest;
f. calibrating, via the computer processor, the seismic dataset to generate a calibrated seismic dataset;
g. computing, via the computer processor, seismic attributes for the calibrated seismic dataset using statistics for AVA classification, wherein the seismic attributes include intercept, gradient and normal vectors; and
h. generating, with the computer processor, direct hydrocarbon indicators as a function of position in the subsurface volume of interest by applying the trained AVA model to the seismic attributes.

2. The computer-implemented method of claim 1 wherein the well logs include at least two of P-wave velocity, shear wave velocity, density, porosity, and V shale .

3. The computer-implemented method of claim 1 wherein the direct hydrocarbon indicators are at least two of lithology, porosity, and fluid type.

4. The computer-implemented method of claim 1 wherein the seismic modeling generates features including at least one of intercept, gradient, AVA class, Normal Vector, Shale Velocity, Acoustic Impedance, Poisson Ratio, and Density, and wherein the features are stored in the AVA database.

5. The computer-implemented method of claim 1 wherein the seismic dataset is comprised of partial angle stacks.

6. The computer-implemented method of claim 1 wherein the statistics are standard z-score statistics.

7. The computer-implemented method of claim 1 further comprising:
a. obtaining, from the non-transient storage medium, additional attributes; and
b. using the additional attributes in addition to the trained AVA model to generate the direct hydrocarbon indicators as a function of position in the subsurface volume of interest.

8. The computer-implemented method of claim 7 wherein the additional attributes are at least one of seismic inversion products and a velocity model.

9. A computer system, comprising:
a computer processor;
non-transient storage medium; and
one or more programs, wherein the one or more programs are stored in the non-transient storage medium and configured to be executed by the computer processor, the one or more programs including instructions that when executed by the one or more processors cause the system to:
a. obtain, from the non-transient storage medium, well logs representative of subsurface volumes of interest;
b. generate, via the computer processor, an amplitude variation with angle (AVA) database from the well logs by seismic modeling, wherein the seismic modeling is performed a plurality of times for all combinations of fluid substitutions of brine, oil, and gas and low porosity, mid-porosity, and high porosity;
c. generate, via the computer processor, a trained AVA model using the AVA database; and
d. store the trained AVA model in the non-transient storage medium;
e. obtaining, from the non-transient storage medium, a seismic dataset representative of a particular subsurface volume of interest;
f. calibrating, via the computer processor, the seismic dataset to generate a calibrated seismic dataset;
g. computing, via the computer processor, seismic attributes for the calibrated seismic dataset using statistics for AVA classification, wherein the seismic attributes include intercept, gradient and normal vectors; and
h. generating, with the computer processor, direct hydrocarbon indicators as a function of position in the subsurface volume of interest by applying the trained AVA model to the seismic attributes.

10. The computer system of claim 9 wherein the well logs include at least two of P-wave velocity, shear wave velocity, density, porosity, and V shale .

11. The computer system of claim 9 wherein the direct hydrocarbon indicators are at least two of lithology, porosity, and fluid type.

12. The computer system of claim 9 wherein the seismic modeling generates features including at least one of intercept, gradient, AVA class, Normal Vector, Shale Velocity, Acoustic Impedance, Poisson Ratio, and Density, and wherein the features are stored in the AVA database.

13. The computer system of claim 9 wherein the seismic dataset is comprised of partial angle stacks.

14. The computer system of claim 9 wherein the statistics are standard z-score statistics.

15. The computer system of claim 9 further comprising:
a. obtaining, from the non-transient storage medium, additional attributes; and
b. using the additional attributes in addition to the trained AVA model to generate the direct hydrocarbon indicators as a function of position in the subsurface volume of interest.

16. The computer system of claim 15 wherein the additional attributes are at least one of seismic inversion products and a velocity model.

Assignees

• Chevron Usa Inc

Inventors

• Roy Prasenjit
• Zhu Xinfa

Classifications

• G01V1/50 Primary

  Analysing data · CPC title

• G01V1/282

  Application of seismic models, synthetic seismograms · CPC title

• G01V1/307

  for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity · CPC title

• G06N20/00

  Machine learning · CPC title

• G01V2210/632

  Amplitude variation versus offset or angle of incidence [AVA, AVO, AVI] · CPC title