Wavelet Transform Coefficient Segmentation for Geological Formation Classification
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Solution Overview
Problem
Geological formations with depositional heterogeneity pose challenges in accurately determining the distribution of reservoir properties, making it difficult to classify stratigraphic, structural, or physical characteristics automatically with high consistency and accuracy.
Innovation Solution
A method involving wavelet transform is applied to geological formation data to derive coefficients, segment the data, determine variability measures, and classify the formation based on these measures, enabling accurate stratigraphic, structural, or physical classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wavelet transform is applied to analyze geological formation data, then measurement precision and classification accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the continuous wavelet transform coefficients into discrete segments corresponding to different geological zones or features. This segmentation allows the complex continuous data to be divided into manageable discrete units for classification, reducing computational complexity while maintaining measurement precision.
Solution Approach 2:
The patent transforms the original geological formation data through wavelet transform, changing the parameter representation from time-domain to frequency-domain coefficients. This parameter transformation enables more accurate characterization of geological heterogeneity while the discrete nature of the transformed coefficients simplifies subsequent processing.
2Productivity
If automatic classification methods are used to analyze geological formations, then productivity and efficiency are improved, but measurement precision and consistency may deteriorate due to loss of nuanced interpretation
Solution Approach 1:
The patent implements a classification system that uses wavelet transform coefficients as feedback features to automatically identify and classify geological formations. The method compares the computed coefficients against reference patterns or thresholds, providing consistent automated decision-making that maintains precision while improving productivity through elimination of manual interpretation variability.
Solution Approach 2:
The patent replaces manual geological interpretation (mechanical human analysis) with an automated computational system based on wavelet transform. This substitution maintains measurement precision by using objective mathematical transformations while dramatically improving productivity through automated processing of large datasets.
Data Source
AI summary
A method for analyzing characteristics of a geological formation includes obtaining at a processor data representative of at least one of stratigraphic, structural, or physical characteristics of the geological formation, applying at the processor a wavelet transform to at least a portion of the obtained data or data interpreted or derived from the obtained data to derive one or more wavelet transform coefficients representative of the obtained data, segmenting at the processor at least one or more of the obtained data or data interpreted or derived from the obtained data into segments, determining at the processor a measure of variability of the obtained data or the data interpreted or derived from the obtained data over each segment at one or more scales of the wavelet transform.