Adaptive Seismic Horizon Tracking via Candidate Path Optimization

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Solution Overview

Problem

Current automated methods for seismic horizon tracking in 3D seismic data face challenges due to lateral continuity issues, leading to inaccuracies and increased time and effort in data interpretation, especially in complex or noisy data, where traditional methods are often reverted to for accuracy.

Innovation Solution

A computer-implemented method and system that adaptively and interactively tracks geological features like horizons and faults in 3D seismic data, using a combination of optimization algorithms such as Shortest-Path and Markov Random Field, allowing for 2D/3D interpretation and quality checking, while maintaining natural fault boundaries and providing alternate route visualization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional automated seed-based tracking methods are used, then tracking speed is improved, but lateral continuity is lost leading to jumps across phase cycles and reduced accuracy

Engineering Contradiction:
Improvetracking speedVSAvoidhorizon tracking accuracy
Core Design Contradiction:
ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the horizon tracking problem into multiple candidate paths, each representing a possible continuous trajectory. By dividing the search space into discrete candidate paths that maintain lateral continuity, the system can evaluate multiple options simultaneously while preserving geological realism, thus resolving the contradiction between speed and accuracy.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent introduces a new dimension of evaluation by considering multiple candidate paths in parallel rather than single-trace sequential tracking. This multi-dimensional approach allows the system to maintain lateral continuity across traces while efficiently evaluating multiple possible horizon trajectories, achieving both speed and accuracy.

Inventive Principle:
Principle #17Another dimension (Dimensionality change)

2Measurement precision

If global approaches considering lateral continuity are used, then horizon tracking accuracy is improved, but computational complexity and processing time increase

Engineering Contradiction:
Improvehorizon tracking accuracyVSAvoidcomputational complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent performs preliminary generation of candidate paths before final selection. By pre-computing multiple possible horizon trajectories that satisfy lateral continuity constraints, the system reduces the complexity of the final optimization step. This preliminary action allows global approaches to achieve high accuracy without excessive computational burden during interactive use.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent generates a limited set of candidate paths (excessive action) rather than evaluating all possible trajectories. This partial enumeration of candidate solutions provides sufficient accuracy for geological interpretation while keeping computational complexity manageable for interactive applications.

Inventive Principle:
Principle #16Partial or excessive action

3Measurement precision

If manual interpretation methods are used, then accuracy in complex data is maintained, but time and effort required for interpretation increases

Engineering Contradiction:
Improveinterpretation accuracyVSAvoidinterpretation time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The patent enables the system to automatically generate and evaluate multiple candidate horizon paths, performing self-service interpretation. The automated system presents multiple geologically plausible options to the interpreter, reducing manual tracing time while maintaining accuracy through the presentation of pre-evaluated candidate solutions that require minimal manual editing.

Inventive Principle:
Principle #25Self-service

4Productivity

If conventional auto-tracking is used, then processing efficiency is improved, but ability to handle complex or noisy data deteriorates

Engineering Contradiction:
Improveprocessing efficiencyVSAvoidrobustness in complex data
Core Design Contradiction:
ProductivityVSReliability

Solution Approach 1:

The patent implements feedback by allowing interpreters to interactively select from multiple candidate paths and provide corrections. The system uses this feedback to refine subsequent tracking, maintaining high processing efficiency while improving reliability in complex data through iterative refinement based on geological expertise.

Inventive Principle:
Principle #23Feedback

Solution Approach 2:

The patent changes the parameter space by considering multiple candidate paths with different characteristics simultaneously. This multi-parameter approach allows the system to adapt to complex or noisy data by selecting the most geologically plausible path from multiple options, maintaining both efficiency and robustness.

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentEP3519865B1Adaptive tracking of geological objects
Publication Date: 2024.08.21 FOSTER FINDLAY ASSOCIATES LTD
  • EP3519865B1 patent drawingFigure 1~2
  • EP3519865B1 patent drawingFigure 3
  • EP3519865B1 patent drawingFigure 4~5

AI summary

The present invention provides acomputer-implemented method for detecting at least one natural contour of a geologic object in 3D seismic data, the method comprising the steps of: (a)receiving at least one first predetermined data set from said 3D seismic data comprising a plurality of phase events; (b)selecting at least one first seed phase event having a first phase characteristic from said plurality of phase events; (c)determine a characterising score between said selected at least one first seed phase event and each one of a predetermined number of candidate phase events of said at least one first predetermined data set; (d)assign said characterising score to each one of said predetermined number of candidate phase events; (e)adjust said characterising score of at least one of said predetermined number of candidate phase events in accordance with at least one first boundary condition; (f)determine at least one natural contour between said at least one first seed phase event and at least a second phase event, utilising an optimisation algorithm; (g)generate a visual representation of said at least one natural contour within said at least one first predetermined data set.