Seismic Horizon Surface Selection via Iterative Scoring
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Solution Overview
Problem
Existing methods for ranking and selecting seismic horizon surfaces or patches in geophysical subsurface imaging data are limited by their reliance on specific parametric scoring functions, which are not applicable to the diverse nature of the problem, and do not allow for the use of virtually any scoring function or consideration of interactions between surfaces.
Innovation Solution
A method that defines a score function for horizon surfaces or patches, calculates scores based on interactions and selection status, iteratively selects surfaces to form a representative subset, and uses this subset for geologic modeling, attribute generation, and hydrocarbon management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If automated horizon mapping methods are used to map all peaks and troughs in a seismic volume, then a dense stack of horizon surfaces is generated, but the computational burden increases and interpretation becomes more difficult
Solution Approach 1:
The patent extracts only the most relevant horizon surfaces from the dense stack generated by automated mapping methods. By applying scoring functions that evaluate geological significance, the method isolates and selects a manageable subset of surfaces that capture the essential geologic features, removing the overwhelming majority of less important surfaces.
Solution Approach 2:
The patent applies different scoring criteria to different regions of the seismic volume based on local geological characteristics. The scoring function adapts to local conditions by weighting different attributes (such as continuity, amplitude, geometric properties) differently in different areas, allowing the selection process to prioritize surfaces with locally significant geological meaning.
2Loss of information
If a dense stack of horizon surfaces is generated, then comprehensive coverage of geologic features is achieved, but the time required for interpretation increases
Solution Approach 1:
The patent performs preliminary scoring and ranking of all horizon surfaces before the actual interpretation process. By pre-evaluating each surface using automated scoring functions that assess geological significance, continuity, and other relevant attributes, the method prepares a pre-ranked list that guides the interpreter's attention to the most important surfaces first, reducing the time needed to achieve comprehensive coverage.
Solution Approach 2:
The patent implements an iterative feedback mechanism where the scoring function is refined based on interpreter feedback and geological knowledge. As interpreters work through the ranked surfaces, their observations and corrections feed back into the scoring system, improving its ability to prioritize relevant surfaces in subsequent iterations, thereby reducing overall interpretation time while maintaining completeness.
3Measurement precision
If all horizon surfaces are analyzed in detail, then accurate geologic modeling is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent applies partial action by performing detailed analysis only on the subset of horizon surfaces that score above a certain threshold of geological significance. Rather than analyzing all surfaces with equal detail, the method applies full analytical rigor only where needed, while using automated scoring to quickly evaluate and filter the remaining surfaces, thus achieving accurate modeling where it matters most while conserving computational resources.
4Adaptability or versatility
If traditional ranking methods are used, then database search results can be optimized, but they cannot handle the diverse nature of seismic horizon surfaces
Solution Approach 1:
The patent creates a universal scoring framework that can evaluate diverse types of horizon surfaces using multiple competing hypotheses. The scoring function is designed to be multi-functional, incorporating various geological criteria (continuity, geometric properties, amplitude characteristics, stratigraphic relationships) that can be applied consistently across different surface types and geological settings, making the method adaptable to the diverse nature of seismic data.
Data Source
AI summary
A method to select a representative subset of a plurality of horizon surfaces or surface patches from geophysical subsurface imaging data, including: defining a score function on one or more horizon surfaces or surface patches; calculating, by a computer, the score for each of the plurality of horizon surfaces or surface patches with regard to other horizon surfaces or surface patches and whether the other horizon surfaces or surface patches have been selected or not for inclusion or exclusion in the subset of the plurality of horizon surfaces; selecting, by a computer, one or more of the plurality of horizon surfaces or surface patches to be included in the subset of the plurality of horizon surfaces or surface patches or excluded from the subset of the plurality of horizon surfaces or surface patches based on their respective scores; iteratively repeating the selecting and calculating steps until a stopping condition is reached and the subset of the plurality of horizon surfaces or surface patches is determined; and performing interpretation on the subset of the plurality of horizon surfaces or surfaces patches.