Grain-e

Grain-e is an advanced digital technology that uses artificial intelligence to analyze wellbore geology from cuttings samples. The process begins with high-resolution photography of washed cuttings samples under controlled lighting conditions. A proprietary AI model then segments the image to identify and outline individual grains. 

The system calculates properties like grain size, roundness, sphericity, and color for thousands of grains per sample. An interactive filtering system allows geologists to refine the output by adjusting parameters like color, size, shape, and visibility to ensure only representative grains are included.

This approach enables consistent, objective sediment description at scale. It provides detailed grain statistics for reservoir characterization, creates color logs for stratigraphic correlation, and can detect features like splintery cavings that indicate high pore pressure zones. 

Key advantages include speed of analysis, consistency across wells and basins, and the ability to quantify properties that were previously difficult to measure at scale. Applications include reservoir characterization, drilling safety through pore pressure prediction, stratigraphy and correlation between wells, and large-scale digitization of physical cuttings samples.

The technology aims to transform how geologists interact with cuttings data by combining AI, high-resolution imagery, and interactive tools to deliver rapid, detailed sedimentological insights from wellbore samples.