Residual Moveout Estimation via Least Squares Inversion

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Solution Overview

Problem

Existing methods for estimating residual moveout in seismic data are either time-consuming or unreliable, particularly when dealing with complex curvatures and high noise levels, which affects the accuracy of subsurface velocity models used in seismic data processing.

Innovation Solution

An automated method using conjugate-gradient least-squares inversion to estimate residual moveout in common-image gathers, flattening the data without assuming hyperbolic or parabolic trajectories, and providing optimized depth residuals for improved velocity model building.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual picking technology is used to estimate residual moveout, then reliability is improved because users can discriminate between signal and noise, but productivity deteriorates because the method is very time consuming

Engineering Contradiction:

Improvereliability of residual moveout estimationVSAvoidspeed of velocity model building

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical picking with an automated computerized system that uses signal processing algorithms. The system automatically identifies and tracks seismic events across traces, computes curvature parameters, and estimates residual moveout without human intervention, thereby maintaining reliability while dramatically improving productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements self-service through automated event detection and tracking algorithms that independently identify seismic events, follow them across multiple traces, and compute residual moveout parameters without requiring user discrimination or manual intervention, enabling the system to serve itself in the residual moveout estimation process

Inventive Principle:

Principle #25Self-service

2Productivity

If automated cross-correlation algorithms are used to estimate residual moveout, then productivity is improved because the method is computationally efficient, but reliability deteriorates because the solutions are sensitive to noise and window size

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidrobustness to noise

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent applies preliminary action by performing automated event detection and tracking before residual moveout estimation. The system pre-identifies seismic events and establishes their continuity across traces, creating a robust foundation that is less sensitive to noise in subsequent curvature computation steps

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the residual moveout estimation process into distinct stages: event detection, event tracking, curvature computation, and parameter estimation. This segmentation allows each stage to be optimized independently, with noise filtering applied at appropriate points, thereby improving overall reliability while maintaining computational efficiency

Inventive Principle:

Principle #1Segmentation

3Ease of operation

If hyperbolic or parabolic curve approximation is used for manual picking, then ease of operation is improved because the method is simple to implement, but measurement precision deteriorates because it cannot accurately capture complex curvatures

Engineering Contradiction:

Improvesimplicity of picking methodVSAvoidaccuracy of depth shift estimation

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent changes the parameter representation from simple hyperbolic/parabolic curvature to a more general polynomial or spline-based curvature model. This allows the system to accurately capture complex residual moveout patterns while maintaining automated operation, thereby improving measurement precision without sacrificing ease of operation

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies dynamics by using adaptive curvature modeling that can adjust its complexity based on the local characteristics of the seismic data. The system dynamically selects appropriate polynomial orders or spline degrees to match the observed residual moveout patterns, providing both simplicity and precision

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS9482770B2Residual moveout estimation through least squares inversion

Publication Date: 2016.11.01 EXXONMOBIL UPSTREAM RESEARCH COMPANY(US)

AI summary

Method for estimating residual moveout in common image gathers (51) of seismic data for use in velocity tomography (57). The method iteratively (56) flattens (55) the common image gathers against a specified reference trace through the application of conjugate-gradient least-squares inversion (53). Different from other picking methods which need to identify and track strong amplitudes, the inventive method automatically inverts for the depth residual for every grid point in the image gather. There is no need to identify and track events.