Abstract
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.