Azimuthal Wellbore Dip Refinement via Hough Transform
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Solution Overview
Problem
Manual dip picking in azimuthal wellbore imaging is time-consuming, tedious, and subjective, with existing automatic methods being unreliable for high-angle and horizontal wellbore data.
Innovation Solution
The system performs a Hough transformation on only a portion of the azimuthal wellbore image, using manual picks as a starting point to refine dips in real-time, reducing computational burden and bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual dip picking is performed, then accuracy can be maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs preliminary automatic dip picking to generate initial dip estimates, which then serve as starting points for iterative refinement. This preliminary action reduces the overall time required while maintaining accuracy by avoiding random initialization and providing informed starting points for optimization.
Solution Approach 2:
The system implements feedback through iterative refinement where initial automatic dip picks are evaluated, and adjustments are made based on error minimization between observed and predicted bed boundaries. This feedback loop continues until convergence, ensuring high accuracy while automating the process.
2Productivity
If full automatic dip picking is implemented, then productivity increases, but reliability decreases for high-angle and horizontal wellbore data
Solution Approach 1:
The iterative refinement process uses feedback from comparing predicted versus actual bed boundaries to continuously improve dip estimates. This feedback mechanism ensures reliability by adjusting parameters until convergence is achieved, making the automatic method as reliable as manual picking for various wellbore orientations.
Solution Approach 2:
The system dynamically adapts the refinement process based on the specific characteristics of each dip pick and wellbore orientation. By adjusting the iterative process to local conditions, it maintains high reliability across different scenarios including high-angle and horizontal wellbores while preserving productivity benefits.
3Measurement precision
If Hough transformation is applied to the entire azimuthal wellbore image, then comprehensive dip analysis is achieved, but computational burden becomes too heavy for real-time use
Solution Approach 1:
The system segments the dip picking process into two parts: a fast initial automatic pick and a targeted iterative refinement. This segmentation avoids applying computationally intensive Hough transformation to the entire image, instead using it only where needed for refinement, thus enabling real-time performance while maintaining comprehensiveness.
Solution Approach 2:
The system applies Hough transformation partially - only for the iterative refinement of specific dip picks rather than to the entire image from scratch. This partial application of the transformation maintains dip detection comprehensiveness where needed while dramatically reducing overall computational time for real-time use.
4Productivity
If conventional automatic dip picking methods are used, then processing speed increases, but accuracy deteriorates for challenging dips from high-angle and horizontal wellbore data
Solution Approach 1:
The iterative refinement process continuously compares predicted bed boundaries with actual observations and adjusts dip parameters to minimize errors. This feedback mechanism maintains high accuracy for challenging dips from high-angle and horizontal wellbores while preserving the processing speed benefits of automation.
Solution Approach 2:
The system performs preliminary automatic dip picking to establish initial estimates quickly, then applies iterative refinement only where needed. This preliminary action maintains processing speed by avoiding exhaustive analysis everywhere, while the subsequent refinement ensures accuracy for challenging dip scenarios.
Data Source
AI summary
Aspects of the subject technology relate to systems and methods of refining manually selected values of physical parameters associated with a manual dip of an azimuthal wellbore image. The method includes perturbating one or more of the manually selected values, selecting a portion of the azimuthal wellbore image based in part on the perturbated values, and performing a Hough transform of the selected portion of the azimuthal wellbore image into a parameter space comprising a plurality of points, thereby determining respective cumulative counts for one or more of the plurality of points in the parameter space. The method also includes selecting a point based in part on its respective cumulative count, calculating refined values of the physical parameters associated with the selected point, and returning the refined values as the physical parameters of a refined dip.