Borehole Log Data Processing via Illumination-Reflectance Separation
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Solution Overview
Problem
Current borehole log data processing methods, particularly normalization techniques, fail to accurately represent resistivity variations and often result in ghosting and loss of features due to their inability to account for spatial distribution and high-contrast transitions, making it difficult for both human and automated analysis.
Innovation Solution
The method involves modeling log data as components of an image, transforming it into a logarithmic domain, performing Fourier transforms, high-pass filtering, and mapping values to color values, which separates illumination and reflectance values, allowing for enhanced dynamic range and reduced ghosting effects, and optionally applying virtual light source and high dynamic range processing to improve image log quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current normalization techniques are used to process borehole log data, then the processing is simple and fast, but the dynamic range is limited and ghosting effects occur
Solution Approach 1:
The patent segments the log data processing into multiple components: illumination component extraction, reflectance component extraction, and separate processing of each component. This segmentation allows the high dynamic range processing to be achieved by handling different components separately, managing the complexity through structured decomposition of the processing pipeline.
Solution Approach 2:
The patent transitions from conventional single-dimension normalization to multi-dimensional processing by separating data into illumination and reflectance components, then processing each in appropriate domains (spatial frequency domain for illumination, color space for reflectance). This dimensional expansion enables preservation of both low-contrast and high-contrast features simultaneously.
2Measurement precision
If conventional normalization is applied to image logs, then the processing is straightforward, but high-contrast transitions cause ghosting and feature loss
Solution Approach 1:
The patent applies preliminary high-pass filtering to the illumination component before final processing. This preliminary action removes high-frequency noise and artifacts from the illumination data before it is combined with the reflectance component, preventing ghosting effects from occurring in the final image log while maintaining processing efficiency through pre-processing.
Solution Approach 2:
The patent introduces an intermediary processing step where the illumination and reflectance components are processed separately through different pipelines (spatial frequency filtering for illumination, color space transformation for reflectance) before being recombined. This intermediary separation allows each component to be optimized independently, improving feature resolution while keeping the overall process manageable through modular design.
3Measurement precision
If static normalization is used across the entire well, then processing is simple, but small but significant resistivity variations are not rendered
Solution Approach 1:
The patent applies local quality enhancement by processing the illumination component through high-pass filtering in the spatial frequency domain, which locally enhances small variations in resistivity values. This allows significant but subtle resistivity changes to be rendered with high precision while maintaining a unified processing framework that manages complexity through consistent local operations applied across the entire well data.
Data Source
AI summary
A method of processing borehole log data to create one or more image logs involve modelling the log data as components of an image in the form i(x, y)=l(x, y)×r(x, y) (1), in which i(x, y) is an image representative of the log data, l(x, y) denotes an illumination value of the image at two-dimensional spatial co-ordinates x, y, and r(x, y) denotes a surface reflectance value at the co-ordinates x, y. Equation (1) is transformed to a logarithmic domain, and a Fourier transform is obtained of the resulting logarithmic domain expression to obtain a Fourier domain expression. The Fourier domain expression is high-pass filtered, and an inverse Fourier transform is obtained of the resulting filtered Fourier domain expression. An exponential operation is performed on the result of inverse Fourier transform to obtain a filtered image model expression. Values of the filtered image model expression are mapped to respective color values across the range of the filtered image model expression values. The mapped color values can then be displayed, printed, saved and/or transmitted as one or more image logs.