Seismic Amplitude Analysis Using Standard Score Statistics
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Solution Overview
Problem
Current seismic amplitude analysis methods are unreliable due to noise in intercept and gradient attributes caused by gather flatness, frequency variations, calibration issues, and side lobes, making it difficult to accurately identify hydrocarbon reservoirs and rock properties from 3D seismic data.
Innovation Solution
The method classifies seismic datasets into background and amplitude variation with angle (AVA) anomaly volumes using standard score statistics, calculates mean amplitudes in sub-cubes, and estimates AVA intercept and gradient values using least squares regression, reducing noise and improving attribute calculation reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If intercept and gradient attributes are calculated trace by trace for every sample, then the attributes are easy to compute, but they become very noisy and unreliable for inferences
Solution Approach 1:
The patent divides the seismic data into multiple angle stacks and further segments each stack into multiple samples. It then calculates intercept and gradient attributes for each segment separately before combining them, which reduces noise while maintaining computational feasibility. This segmentation approach allows the system to process complex 3D seismic data in manageable portions, improving reliability without sacrificing ease of computation.
Solution Approach 2:
The patent merges multiple angle stacks and their corresponding intercept and gradient attributes into a unified analysis framework. By combining data from multiple angles and stacking them in a systematic way, the system achieves more reliable AVA attributes while maintaining computational efficiency through automated processing of the merged data structure.
2Reliability
If horizon slices are used to ensure tracking of same seismic event, then AVA classification is improved, but the process becomes time consuming and prone to picking errors
Solution Approach 1:
The patent implements automated processing where the system automatically identifies and tracks seismic events across angle stacks without requiring manual horizon picking. The automated algorithms self-adjust to track seismic events through the 3D volume, eliminating time-consuming manual intervention and picking errors while maintaining accurate event tracking and AVA classification.
Solution Approach 2:
The patent replaces manual horizon slicing and picking processes with automated computational algorithms. Instead of mechanically manually defining horizons, the system uses automated signal processing and statistical methods to identify and track seismic events, significantly reducing time requirements while improving consistency and accuracy.
3Device complexity
If conventional algorithms rely on single or limited samples, then computation is simplified, but gradient estimation becomes very noisy in low amplitude side lobes
Solution Approach 1:
The patent merges multiple samples from different angle stacks into a comprehensive analysis. Instead of relying on single or limited samples, the system combines data from multiple angles and stacks to calculate intercept and gradient attributes, which significantly improves gradient estimation precision even in low amplitude side lobes while maintaining reasonable algorithmic complexity through systematic processing.
Data Source
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AI summary
A method is described for seismic amplitude analysis including receiving a seismic dataset representative of a subsurface volume of interest wherein the seismic dataset includes an angle or angle stack dimension; select a plurality of sets of sub-cubes in the seismic dataset wherein each set of sub-cubes includes a plurality of the angles or the angle stacks; compute standard score statistics for each of the plurality of sub-cubes; identify amplitude variation with angle (AVA) anomalies based on the standard score statistics for each of the set of sub-cubes; classify the AVA anomalies to generate classified AVA anomalies; and displaying, on a user interface, the classified AVA anomalies as a graphical display. The method is executed by a computer system.