Borehole Image Texture Analysis for Facies Identification
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Solution Overview
Problem
Current methods for analyzing borehole images require extensive expertise and time to identify texture boundaries and facies in geological formations, limiting the efficiency of reservoir evaluation in the oil industry.
Innovation Solution
A method that determines the local apparent dip of a borehole image, applies windows shaped according to the dip, compares textures within these windows, and identifies texture boundaries to automatically or semi-automatically segment and cluster the image, facilitating the determination of facies and reducing analysis time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If borehole images are analyzed by highly qualified geologists using manual observation and classification, then the accuracy of facies identification is improved, but the analysis time and operational complexity increase significantly
Solution Approach 1:
The patent replaces the manual mechanical observation and classification process with an automated image processing system that uses texture analysis algorithms, dip calculation, and pattern recognition to identify facies and bed boundaries, thereby eliminating the time-consuming manual analysis while maintaining identification accuracy
Solution Approach 2:
The patent creates a digital representation of the borehole image and processes this copy through automated algorithms including dip calculation, texture feature extraction, and pattern matching, allowing rapid analysis without requiring physical manual examination of the original images
2Ease of manufacture
If conventional logs are used to characterize rock type through electrofacies, then the measurement process is simplified, but the descriptive accuracy of rock properties deteriorates
Solution Approach 1:
The patent combines the simplicity of conventional log measurements with the descriptive power of borehole image texture analysis by integrating electrofacies data with automated texture-based facies identification, creating a composite approach that maintains both ease of measurement and high descriptive accuracy of rock properties
Solution Approach 2:
The patent makes the borehole image analysis system multi-functional by enabling it to perform both quantitative measurements (similar to conventional logs) and qualitative texture-based classification simultaneously, allowing the same system to provide both simplified measurement and detailed rock property description
3Productivity
If automated image analysis methods are implemented, then the analysis time and operational complexity are reduced, but the computational requirements and algorithm complexity increase
Solution Approach 1:
The patent divides the complex image analysis task into distinct sequential segments: dip calculation, window application, texture feature extraction, pattern recognition, and facies identification. This segmentation allows each sub-task to be processed independently with optimized algorithms, reducing overall computational complexity while maintaining high analysis efficiency
Solution Approach 2:
The patent performs preliminary processing steps including dip calculation and window application before the main texture analysis, preparing the data in advance to simplify subsequent processing. This preliminary action reduces the computational burden during the main analysis phase, improving overall efficiency without requiring overly complex real-time processing
Data Source
AI summary
The disclosure relates to methods and systems for analyzing an image of the formation intersected by a borehole. One of the methods determines a local apparent dip of the borehole at least at a measured depth i represented on the image, applies at least a window to the image, wherein each of the windows includes one of the measured depth i and is shaped as a function of the determined local dip at the corresponding measured depth i, compares a texture of at least a first zone of each window and a texture of at least a second zone of said window, wherein each of the first and second zones are adjacent and shaped as a function of the determined dip. Based on the comparison, the method determines at least a location of a texture boundary and derives a property of the formation. The other method includes determine locations of the texture boundaries, segmenting the image as a function of the texture boundaries, and perform clustering of the segments in order to determine a facies of the formation.