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Information extraction from historical well records using a large language model.

Zhiwei Ma1, Javier E Santos2, Greg Lackey3

  • 1Earth & Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87544, USA. m.ma@lanl.gov.

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Summary
This summary is machine-generated.

This study introduces a computational method using large language models (LLMs) to extract data from historical oil and gas well records. This approach efficiently locates orphaned wells, improving environmental risk reduction efforts.

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Area of Science:

  • Environmental Science and Engineering
  • Geoscience Information Management
  • Artificial Intelligence in Geosciences

Background:

  • Orphaned wells (abandoned oil and gas wells) pose significant environmental risks.
  • Manual data extraction from historical well records is time-consuming and inefficient.
  • Accurate well location and depth data are crucial for effective plugging and remediation.

Purpose of the Study:

  • To develop and evaluate a computational approach for rapid and cost-effective characterization of orphaned wells.
  • To leverage large language models (LLMs) for extracting vital information from historical well documents.
  • To automate the digitization of geoscientific data from legacy oil and gas records.

Main Methods:

  • An information extraction workflow was developed using open-source Llama 2 large language models.
  • The workflow was tested on a dataset of 160 historical well documents.
  • Performance was evaluated based on accuracy in extracting well location and depth, comparing PDF-based and image-based records.

Main Results:

  • The LLM-based workflow achieved 100% accuracy for clean, PDF-based well reports.
  • Accuracy decreased to 70% when processing unstructured, image-based well records.
  • The automated approach significantly reduces labor and increases efficiency compared to manual digitization.

Conclusions:

  • Large language models offer a powerful tool for accessing and utilizing vast amounts of geoscientific information stored in historical documents.
  • The developed workflow demonstrates the potential for LLMs to accelerate the identification and characterization of orphaned wells.
  • Further improvements in handling unstructured data are needed for broader application, but the benefits in automation and labor reduction are substantial.