Seismic Horizon Surface Modeling with Spline Functions

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Solution Overview

Problem

Existing methods for determining seismic horizon surfaces in seismic images are computationally inefficient and fail to accurately account for prior knowledge about geological formations, particularly in the presence of faults, leading to potential misidentification of isochronous surfaces.

Innovation Solution

A method using spline functions, specifically B-splines and non-uniform rational B-splines (NURBS), to model seismic horizon surfaces, allowing for a less dense grid representation and iterative alignment with local seismic dips, while incorporating prior knowledge through setpoints and spline quasi-interpolant operators.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If a regular grid is used for seismic image processing to enable fast Fourier transforms, then computational complexity is reduced, but the ability to incorporate prior knowledge about geological formations is lost

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidability to incorporate prior knowledge

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The seismic horizon surface is segmented into multiple control points that can be independently adjusted. This allows the system to work with sparse data points rather than requiring a dense regular grid, enabling both computational efficiency and the incorporation of prior geological knowledge through strategically placed control points.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms the problem from working with a dense regular grid of parameters to working with a sparse set of control points with specific geological constraints. This parameter transformation enables the system to incorporate prior knowledge about fault locations and reflector positions while reducing computational complexity.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If a dense grid is used to ensure accurate representation of seismic reflectors, then measurement precision is improved, but computational complexity increases

Engineering Contradiction:

Improveaccuracy of seismic horizon surfaceVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Instead of processing the entire seismic volume with uniform high density, the patent applies dense sampling only at critical locations (control points near faults and reflectors) and sparse sampling elsewhere. This partial action approach maintains measurement precision where needed while reducing overall computational complexity.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent applies different levels of processing density to different regions of the seismic data. Areas with complex geological features (faults, reflectors) receive higher density control points and more rigorous processing, while homogeneous regions use sparser sampling. This local quality differentiation maintains accuracy for critical features while reducing computational burden.

Inventive Principle:

Principle #3Local quality

3Speed

If traditional methods are used to determine seismic horizon surfaces, then computational speed is maintained, but reliability of isochronous surface identification decreases in the presence of faults

Engineering Contradiction:

Improvecomputational speedVSAvoidaccuracy of isochronous surface identification

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The patent performs preliminary identification of fault locations and reflector positions before the main horizon surface determination process. Control points are pre-positioned at these critical locations with appropriate constraints, so that when the iterative optimization runs, it starts from a geologically informed initial state rather than a random or uniform grid state. This preliminary action improves reliability without significantly increasing computational time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent incorporates geological constraints as feedback mechanisms during the iterative optimization process. The algorithm continuously checks whether the determined horizon surfaces satisfy geological requirements (e.g., continuity across faults, alignment with known reflectors) and adjusts control point positions and weights accordingly. This feedback loop ensures reliability while maintaining computational efficiency through targeted corrections rather than exhaustive processing.

Inventive Principle:

Principle #23Feedback

Data Source

PatentEP4222536B1Method and system for processing seismic images to obtain seismic horizon surfaces for a geological formation

Publication Date: 2024.09.11 TOTAL ENERGIES ONETECH

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AI summary

The present disclosure relates to a computer implemented method (30) for processing a seismic image comprising seismic values obtained from seismic measurements performed on a geological formation, characterized in that said method comprises: - (S30) determining a seismic dip image based on the seismic image, said seismic dip image comprising local seismic dips representative of the local gradient of the seismic values of the seismic image; - (S31) initializing a seismic horizon surface modeled by using spline functions or by using a triangle mesh; - (S32) iteratively modifying the seismic horizon surface to progressively increase alignment between local orientations of the seismic horizon surface and the corresponding local seismic dips, until a predetermined stop criterion is satisfied.