A framework for an AI pipeline for borehole data

Researchers analyzing data collected from borehole drilling projects can face dozens of terabytes of seismic, hydrologic, geologic, and rock mechanics data, including complex imagery, physical measurements, and expert-written reports. These diverse data sets play a pivotal role in understanding solid Earth processes. Ingesting and analyzing such data presents a colossal challenge that typically demands a team of experts and large amounts of time. The utilization of Artificial Intelligence (AI) and machine learning emerges as a compelling approach to help tackle the volume and complexity of drilling data. This paper presents an AI-based pipeline for ingesting data from the Oman Drilling Project’s Multi-borehole Observatory. The study focuses on the alteration of peridotite core segments taken from Borehole BA1B, utilizing a catboost classification model trained on an integrated data set of machine learning segmented core images, physical measurements, geological, lithographic data, and AI-summarized expert texts and feature selection. This paper’s central objective is to establish a repeatable, efficient pattern for processing such multifaceted borehole data through connecting fracture networks detected in the borehole BA1B imagery to the host rock alteration.