Dip Picking and Zonation for Highly Deviated Well Images
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Solution Overview
Problem
Current methods for dip picking and zonation of borehole images, especially in highly deviated or horizontal wells, face challenges due to the complexity of boundary shapes and the sensitivity of dip measurements to borehole geometry and trajectory, leading to inaccuracies and inefficiencies in determining structural dips and zone boundaries.
Innovation Solution
A computer-based method and system that perform dip picking by identifying multiple dips simultaneously, using symmetry probability computation feedback to ensure accuracy and consistency, and computing continuous structural dips and zone boundaries, allowing for efficient and accurate zonation across various well types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dip picking methods are used on highly deviated well images, then the processing is simpler, but the measurement precision of structural dips deteriorates due to sensitivity to borehole geometry and trajectory
Solution Approach 1:
The patent transforms the dip picking problem from 2D image space to 3D wellbore coordinate space by incorporating borehole geometry parameters (trajectory, inclination, azimuth) and transforming boundary coordinates accordingly. This parameter transformation allows accurate structural dip measurement in highly deviated wells by accounting for the complex wellbore path through mathematical coordinate systems and transformation matrices.
Solution Approach 2:
The patent moves from traditional 2D borehole image analysis to 3D spatial analysis by integrating wellbore trajectory data and transforming boundary coordinates into three-dimensional space. This dimensional expansion enables accurate dip measurement in highly deviated wells by considering the spatial relationship between the wellbore path and geological boundaries.
2Productivity
If complex boundary shapes are analyzed using traditional methods, then the manufacturing precision of zone boundaries is maintained, but the productivity of zonation process deteriorates
Solution Approach 1:
The patent performs preliminary transformation of boundary coordinates from 2D image space to 3D wellbore coordinate space before conducting dip picking and zonation. By pre-processing the boundary data with borehole geometry transformations, the system enables efficient automated processing of complex boundaries while maintaining accuracy, as the coordinate transformation is performed once and reused throughout the analysis.
Solution Approach 2:
The patent creates a universal processing framework that handles various boundary shapes (planar, curved, irregular) and well types (vertical, deviated, horizontal) through a single integrated system. The method uses general coordinate transformation equations and dip picking algorithms that automatically adapt to different boundary complexities without requiring separate processing procedures.
3Reliability
If multiple dips are identified simultaneously for each boundary, then the reliability of dip picking is improved, but the device complexity of the system increases
Solution Approach 1:
The patent implements an iterative dip picking process where multiple candidate dips are identified for each boundary, evaluated against consistency criteria (symmetry probability, geometric constraints), and refined through feedback loops. The system calculates dip candidates, assesses their reliability using symmetry metrics, and adjusts the selection to ensure consistent structural interpretation across the entire wellbore image.
Solution Approach 2:
The patent uses symmetry probability computation as a feedback mechanism to evaluate dip picking reliability. By calculating how symmetrically the identified dip aligns with the boundary geometry and geological constraints, the system objectively assesses dip quality and consistency, preferring asymmetric corrections that improve overall structural coherence.
Data Source
AI summary
In one embodiment, a computer-based method includes obtaining a borehole image deriving from a downhole tool in a borehole of a geological formation, performing dip picking on the borehole image to derive one or more structural dips, deriving a continuous structural dip based on the one or more structural dips, defining one or more locations of zone boundaries on the borehole image, deriving one or more zone boundaries based on the continuous structural dip and the one or more locations of zone boundaries, and defining one or more zones of the wellbore in a second image based on the one or more zone boundaries.