Automated Saw Cut Correction for 3D Core Digital Modeling
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Solution Overview
Problem
The process of modeling a core sample using computerized tomography scanner images is time-consuming and prone to inaccuracies due to subjective decision-making and variable adjustments required for image manipulation.
Innovation Solution
Automated techniques for image registration, surface artifact removal, and saw-cut correction are implemented to enhance the accuracy and efficiency of modeling a core sample, including aligning images along an appropriate axis, cropping noise, and generating a three-dimensional model that depicts the internal composition of the core sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated techniques are implemented for image manipulation and modeling, then productivity and accuracy are improved, but device complexity increases
Solution Approach 1:
The system performs automated image registration, saw-cut correction, and artifact removal without requiring manual intervention. The algorithms self-correct distortions by detecting features in the CT images and applying appropriate transformations, allowing the system to service itself rather than requiring operator involvement for each correction step.
Solution Approach 2:
The system automatically adjusts multiple parameters including image alignment transformations, correction magnitudes for saw-cut artifacts, and filtering parameters for noise removal. These parameter changes are performed dynamically based on the specific characteristics of each core sample image set, enabling adaptive processing that improves productivity while managing complexity through algorithmic control.
2Manufacturing precision
If manual image manipulation is performed, then flexibility and adaptability are maintained, but measurement precision and manufacturing precision deteriorate due to subjective decision-making
Solution Approach 1:
The system incorporates feedback mechanisms where the automated algorithms analyze the CT images, detect distortions and artifacts, and iteratively refine corrections based on detected features. The system provides feedback on the quality of corrections made and allows for verification against original image characteristics, ensuring high precision while reducing subjective manual intervention.
Solution Approach 2:
The patent replaces manual mechanical image manipulation with automated computational algorithms. Instead of operators physically adjusting and manipulating images, computer-based algorithms perform registration, correction, and filtering operations, eliminating human subjectivity and improving precision while maintaining operational capability through software automation.
3Measurement precision
If comprehensive image processing steps are performed including registration, artifact removal, and correction, then measurement precision is improved, but loss of time increases due to multiple processing steps
Solution Approach 1:
The system performs preliminary automated assessments of the CT images to identify the types and magnitudes of distortions present. By pre-detecting saw-cut artifacts, misalignment issues, and noise characteristics before full processing begins, the system can optimize the processing pipeline and execute corrections more efficiently, reducing overall processing time while maintaining precision.
Solution Approach 2:
The patent combines multiple image processing operations into an integrated automated workflow. Image registration, saw-cut correction, and artifact removal are merged into a single coordinated process rather than separate manual steps. This consolidation reduces the time required by eliminating transitions between separate operations and allowing parallel processing where applicable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The automated methods reduce inaccuracies and increase time efficiency in modeling core samples, producing more accurate three-dimensional models of core samples for use in reservoir modeling and simulations.
Implementation Method 1
One such technique is to use electrode pads placed against the wellbore wall around the wellbore to force a current through the rock; sensors can then measure the current and map resistivities of the material surrounding the wellbore.
Implementation Method 2
Digital rock samples—digital representations of core samples or other rock samples—can be constructed from image sets obtained by, for example, x-ray computed tomography (CT) scan (CTS), micro-CT scan, or confocal microscopy.
Data Source
AI summary
Computer-implemented methods, systems, and non-transitory computer-readable medium having computer program stored therein are provided to enhance the accuracy and efficiency of modeling a core sample from two-dimensional images of the core sample. Embodiments of the invention include, for example, image registering a plurality of images of transverse sections of a core sample to produce aligned transverse sections and performing a saw cut correction on the aligned images to adjust the images for a slab cut. Embodiments of the saw cut correction can include, for example, identifying the saw cut line, approximating the slab cut boundary, and moving a portion of the representation of the image to the periphery of the approximated slab cut boundary. Embodiments can further include, generating three-dimensional models of the core sample and the internal composition of a borehole related to the core sample using the adjusted saw cut line images and multipoint statistics calculations.