AI Rock Particle Segmentation for Drilling Analysis
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Solution Overview
Problem
The analysis of rock particle images obtained during drilling is time-consuming and subjective, requiring human intervention, which hinders efficient drilling process control and geologic formation characterization.
Innovation Solution
A system utilizing an Artificial Intelligence (AI) model, specifically a Large Foundation Model (LFM), for automated image processing and segmentation of rock particle images, enabling efficient characterization and control of the drilling process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human geologists manually analyze rock particle images in a lab, then analysis accuracy can be maintained through expert observation, but the process is time-consuming and reduces drilling efficiency
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated AI-based image processing system. The AI model automatically detects, segments, and characterizes rock particles from images, eliminating the need for human geologists to manually examine each particle. This substitution of mechanical manual inspection with automated digital processing directly reduces analysis time and increases drilling efficiency.
Solution Approach 2:
The AI system performs self-service analysis by automatically processing rock particle images without requiring human intervention. The system independently completes image acquisition, particle detection, segmentation, and characterization tasks, enabling the system to serve itself rather than requiring human experts to perform the analysis manually.
2Reliability
If human geologists subjectively interpret rock particle images, then analysis can be performed with existing expertise, but the subjectivity introduces variability and reduces reliability
Solution Approach 1:
The patent replaces subjective human interpretation with objective AI-based automated analysis. The AI model provides consistent, reproducible results by applying the same algorithmic criteria to each image, eliminating the variability introduced by different geologists' expertise and judgment. This ensures reliable and consistent analysis across all rock particle samples.
3Measurement precision
If manual rock particle analysis is performed away from the drilling installation, then detailed analysis can be conducted in a controlled lab environment, but it creates a disconnect from real-time drilling operations
Solution Approach 1:
The AI system enables real-time self-service analysis at the drilling site by processing images immediately as they are captured. This eliminates the need to transport samples to a remote lab, allowing the system to perform detailed lithology characterization in situ and provide immediate feedback for real-time drilling control decisions.
Solution Approach 2:
The system performs preliminary analysis actions automatically and in real-time during the drilling process. By conducting image acquisition, particle detection, segmentation, and characterization immediately at the source rather than deferred to a later lab analysis, the system enables real-time control while maintaining measurement precision.
Data Source
AI summary
Systems and methods are provided for analyzing sample images, such as for rock particles obtained during drilling of a geologic formation. The system and techniques utilize a Large Foundation Model (LFM) in the segmentation of rock particles. The LFM can receive an image (or image data) of rock particles as an input and generates segmentation of the image at a pixel level (i.e., each pixel of the image is classified) as a segmented image. Additionally, active annotation can be provided in conjunction with a graphics user interface (GUI) to allows for user interaction with images as well as selective segmentation of the images.