  To reduce environmental risks and impacts from orphaned wells (abandoned oil
and gas wells), it is essential to first locate and then plug these wells.
Although some historical documents are available, they are often unstructured,
not cleaned, and outdated. Additionally, they vary widely by state and type.
Manual reading and digitizing this information from historical documents are
not feasible, given the high number of wells. Here, we propose a new
computational approach for rapidly and cost-effectively locating these wells.
Specifically, we leverage the advanced capabilities of large language models
(LLMs) to extract vital information including well location and depth from
historical records of orphaned wells. In this paper, we present an information
extraction workflow based on open-source Llama 2 models and test them on a
dataset of 160 well documents. Our results show that the developed workflow
achieves excellent accuracy in extracting location and depth from clean,
PDF-based reports, with a 100% accuracy rate. However, it struggles with
unstructured image-based well records, where accuracy drops to 70%. The
workflow provides significant benefits over manual human digitization,
including reduced labor and increased automation. In general, more detailed
prompting leads to improved information extraction, and those LLMs with more
parameters typically perform better. We provided a detailed discussion of the
current challenges and the corresponding opportunities/approaches to address
them. Additionally, a vast amount of geoscientific information is locked up in
old documents, and this work demonstrates that recent breakthroughs in LLMs
enable us to unlock this information more broadly.
