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A framework for an AI pipeline for borehole data
  • +2
  • John M. Aiken,
  • Elliot Dufournet,
  • Hamed Amiri,
  • Lotta Ternieten,
  • Oliver Plümper
John M. Aiken
University of Oslo

Corresponding Author:[email protected]

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Elliot Dufournet
University of Oslo
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Hamed Amiri
Utrecht University
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Lotta Ternieten
Department of Ocean Systems, Royal Netherlands Institute for Sea Research
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Oliver Plümper
Utrecht University
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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.