Machine Learning for Thin Section Analysis

10x efficiency gain and improved classification
using deep learning

 

Client: A Major Independent

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Thin section classification tool

The thin section classification tool provides the petrophysicist an intuitive interface to visualize and navigate through the multidimensional image stacks. The petrophysicist can then quickly interpret and label individual grains, providing an interpretation of the mineralogy. This interpretation is then used to create labeled dataset to train deep learning models for automated classification.

Analyze thin sections for granulometry and mineralogy

Entire thin sections can be analyzed for granulometry and mineralogy. The tool allows petrophysicists to analyze entire thin section specimens across an entire field quickly and reproducibly. Shown here are histograms of grain size, orientation, aspect ratio, and circularity.

Challenge

 

Thin sections provide the closest examination of in situ rock properties, essential for accurate reservoir characterization and reserves estimates. Standard analysis techniques such as point counting yield the types of constituent minerals and their shape characteristics, however this is a time consuming and labor intensive procedure. Typically only a small region of the entire thin section specimen can be characterized, and hundreds of specimens can be generated from a single well core.

Modern microscopes generate high resolution images of thin sections at multiple polarization angles. These gigapixel images provide a massive dataset of texture and color features that can be used to extract mineralogy and grain size statistics. The size of this data makes it impossible to assimilate manually.

The challenge is to create an AI-based assistant that gives petrophysicists the ability to quickly visualize and analyze hundreds of thin section images.

 

Solution

 

A data processing pipeline was created for reading raw image data directly from the microscope output. Each dataset consisted of a stack of high resolution images at multiple polarization angles. These were then used as features to distinguish between different minerals and grains. Each dataset was aligned and tiled for efficient viewing. A custom tool was created to provide an intuitive interface to visualize and navigate through the multidimensional image stacks. Controls were provided to allow a petrophysicist to easily label individual grains, providing an interpretation of the mineralogy. This interpretation was used to create a labeled dataset to train deep learning models for automated classification.

The trained models were then used to segment and identify individual grains and porosity. Various statistics of grain shape and mineralogy were extracted over the entire set of thin sections.

 

Result

 

The result was a tool that allowed petrophysicists to analyze entire thin section specimens across an entire field quickly and reproducibly. The analysis results enabled objective comparison of thin sections across wells, and development of well to well correlations. This provided additional data to constrain facies relationships in wells where no whole core was taken.

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