Next-Generation Reservoir Layer Identification powered by Image Segmentation

Based on Patent Research | CN-112182966-A (2024)

Accurate identification of biologically disturbed reservoir layers is essential for effective production planning. Manual interpretation of logging data often leads to inconsistent results and slow decision making. Image segmentation provides a reliable alternative by classifying pixels to map these complex geological zones within high resolution images. This technology precisely delineates disturbed areas to improve extraction strategies. Adopting this automated approach reduces human error and ensures a more detailed understanding of reservoir properties for better site management.

SOLUTION OVERVIEW

Manual Logging Transformed by AI Segmentation

Image segmentation serves as a powerful diagnostic tool for petroleum engineers by dividing borehole images into distinct, meaningful clusters. This process begins by ingestng high resolution logging data into an algorithm that evaluates every individual pixel based on its unique visual characteristics. The system identifies subtle textures and patterns within the rock face, separating disturbed biological zones from stable reservoir layers. This granular analysis produces a detailed map of the subsurface, giving operators a precise visual guide of the entire wellbore structure.

By automating these geological assessments, the technology integrates seamlessly into existing logging workflows to provide rapid insights. This capability is like using a medical MRI scan to pinpoint exact tissue damage instead of relying on external symptoms alone. Such automation reduces the burden on human analysts while providing a more consistent baseline for characterizing site potential. Ultimately, these advancements lead to more informed drilling decisions and optimized resource recovery, ensuring that complex extraction projects remain both sustainable and productive for the long term.

HOW IT WORKS

Analyzing Logs for Layer Identification

Processing High Resolution Borehole Data

The system begins by ingesting high resolution logging data, such as FMI resistivity images, to establish a digital representation of the subsurface. This initial phase transforms raw borehole measurements into a structured format ready for deep visual inspection.

Analyzing Microscopic Pixel Characteristics

Advanced computer vision algorithms evaluate every individual pixel to identify subtle textures and structural anomalies that signify biological disturbance. By analyzing these complex patterns at a granular level, the system detects features that are often missed during manual visual inspections.

Segmenting Distinct Geological Clusters

The AI clusters pixels into specific categories to differentiate between disturbed biological zones and stable reservoir layers. This automated classification process ensures a consistent and objective interpretation of the geological formations surrounding the wellbore.

Generating Detailed Subsurface Maps

The final output provides a comprehensive visual map that outlines the precise boundaries of critical reservoir features for petroleum engineers. These detailed insights allow operators to optimize site management and improve the accuracy of long term extraction strategies.

Potential Benefits

Enhanced Precision in Reservoir Mapping

Automated image segmentation identifies subtle geological textures with pixel-level accuracy, providing a more reliable map of disturbed reservoir zones than manual data interpretation.

Increased Operational Efficiency

By integrating seamless automation into existing logging workflows, the system provides rapid insights that accelerate production planning and reduce the time spent on manual analysis.

Consistent Objective Geological Analysis

The AI eliminates human subjectivity from the evaluation process, establishing a standardized baseline for characterizing wellbore structures across various extraction sites and complex geological formations.

Optimized Long Term Resource Recovery

Detailed subsurface mapping allows petroleum engineers to make more informed drilling decisions, ensuring sustainable extraction strategies and improved productivity throughout the entire lifecycle of the reservoir.

Implementation

1 Data Ingestion Pipeline. Establish a direct connection to borehole logging databases to import high resolution FMI resistivity images into the system.

2 Preprocessing and Calibration. Apply digital filters to raw logging data to normalize pixel intensity and prepare images for detailed textural analysis.

3 Algorithm Configuration. Configure the image segmentation parameters to align with specific geological characteristics and expected biological disturbance patterns.

4 Workflow Integration. Embed the automated segmentation tool into existing petroleum engineering software for seamless subsurface data interpretation.

5 Output Visualization. Generate comprehensive subsurface maps that delineate reservoir layers to guide immediate drilling and extraction strategies.

Source: Analysis based on Patent CN-112182966-A "Biological disturbance reservoir layer identification method based on multi-source logging data" (Filed: August 2024).

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