An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments
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 constrain...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2025JH000666 |
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| author | John M. Aiken Elliot Dufornet Hamed Amiri Lotta Ternieten Oliver Plümper |
| author_facet | John M. Aiken Elliot Dufornet Hamed Amiri Lotta Ternieten Oliver Plümper |
| author_sort | John M. Aiken |
| collection | DOAJ |
| description | 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 constraining solid‐Earth processes. Ingesting and analyzing such data presents a colossal challenge that typically demands a team of experts and a lot of time. Artificial intelligence (AI) and machine learning have emerged as compelling approaches to tackle 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 gradient‐boosted trees (CatBoost) regression 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 aims to establish a repeatable and efficient pattern for processing such multifaceted data from the well. We present results using the data set generated from BA1B. First, we examine the relationship between fracture/vein networks and peridotite alteration, a stand‐in for historical fluid flows. Here we demonstrate that we do not find a strong relationship between these networks and alteration. Then we examine the very strong and also nonlinear relationship between alteration and the magnetic susceptibility and resistivity measured in BA1B. |
| format | Article |
| id | doaj-art-44da2d00c3df4c03beda47b73d582b82 |
| institution | OA Journals |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-44da2d00c3df4c03beda47b73d582b822025-08-20T02:21:10ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2025JH000666An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite EnvironmentsJohn M. Aiken0Elliot Dufornet1Hamed Amiri2Lotta Ternieten3Oliver Plümper4Njord Centre Departments of Physics and Geosciences University of Oslo Oslo NorwayNjord Centre Departments of Physics and Geosciences University of Oslo Oslo NorwayDepartment of Earth Sciences Utrecht University Utrecht The NetherlandsDepartment of Earth Sciences Utrecht University Utrecht The NetherlandsDepartment of Earth Sciences Utrecht University Utrecht The NetherlandsAbstract 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 constraining solid‐Earth processes. Ingesting and analyzing such data presents a colossal challenge that typically demands a team of experts and a lot of time. Artificial intelligence (AI) and machine learning have emerged as compelling approaches to tackle 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 gradient‐boosted trees (CatBoost) regression 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 aims to establish a repeatable and efficient pattern for processing such multifaceted data from the well. We present results using the data set generated from BA1B. First, we examine the relationship between fracture/vein networks and peridotite alteration, a stand‐in for historical fluid flows. Here we demonstrate that we do not find a strong relationship between these networks and alteration. Then we examine the very strong and also nonlinear relationship between alteration and the magnetic susceptibility and resistivity measured in BA1B.https://doi.org/10.1029/2025JH000666peridotite alterationmachine learningchatgptfracture networksreaction driven cracking |
| spellingShingle | John M. Aiken Elliot Dufornet Hamed Amiri Lotta Ternieten Oliver Plümper An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments Journal of Geophysical Research: Machine Learning and Computation peridotite alteration machine learning chatgpt fracture networks reaction driven cracking |
| title | An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments |
| title_full | An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments |
| title_fullStr | An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments |
| title_full_unstemmed | An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments |
| title_short | An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments |
| title_sort | ai enabled data processing pipeline for ingesting borehole data in peridotite environments |
| topic | peridotite alteration machine learning chatgpt fracture networks reaction driven cracking |
| url | https://doi.org/10.1029/2025JH000666 |
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