Zero-Shot Learning With Large Language Models Enhances Drilling-Information Retrieval
Authors: Carpenter Chris 
Published: January 2025

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 217671, “Enhancing Information Retrieval in the Drilling Domain: Zero-Shot Learning With Large Language Models for Question Answering,” by Felix J. Pacis, SPE, University of Stavanger, and Sergey Alyaev and Gilles Pelfrene, SPE, NORCE, et al. The paper has not been peer reviewed.

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Finding information across multiple databases, formats, and documents remains a manual job in the drilling industry. Large language models (LLMs) have proven effective in data-aggregation tasks, including answering questions. However, using LLMs for domain-specific factual responses poses a nontrivial challenge. The expert-labor cost for training domain-specific LLMs prohibits niche industries from developing custom question-answering bots. The complete paper tests several commercial LLMs for information-retrieval tasks for drilling data using zero-shot in-context learning. In addition, the model’s calibration is tested with a few-shot multiple-choice drilling questionnaire.

Introduction

While LLMs have proven effective in various tasks ranging from sentiment analysis to text completion, using LLMs for question-answering tasks presents a challenge in providing factual responses. Pretrained LLMs only serve as a parameterized implicit knowledge base and cannot access recent data; thus, information is bounded by the time of training.

Retrieval augmented generation (RAG) can address some of these issues by extending the utility of LLMs to specific data sources. Fig. 1 shows a simplified RAG-based LLM question/answer application. RAG involves two primary components: document retrieval (green boxes), which retrieves the most relevant context based on the query, and LLM response generation (blue boxes). During the response generation, LLM operates based on the prompt, query, and retrieved context without any change in the model parameters, a process the authors term as “in-context learning.”

Methodology

Two experiments have been conducted: The first one is a few-shot multiple-choice experiment evaluated using the SLB drilling glossary; the second is a zero-shot in-context experiment evaluated on drilling reports and company reports.

Multiple-Choice Experiment.

SLB Drilling Glossary.

For the multiple-choice experiment, a publicly available drilling glossary served as a basis for evaluation. A total of 409 term/definition pairs were considered. Five term/definition pairs were chosen, serving as few-shot default values, while the remaining 404 pairs served as the multiple-choice questions. Four choices were given for each term/definition question pair, where one was the correct answer. The three incorrect choices were picked randomly from all possible terms minus the true answer.

Zero-Shot In-Context Experiment.

Norwegian Petroleum Directorate (NPD) Database.

The authors explored the wellbore history of all individual exploration wells drilled in the Norwegian shelf in the NPD database. In this experiment, 12 exploration wells were randomly chosen for evaluation. In addition to these drilling reports, information about the stratigraphy of three additional wells was added.

Annual Reports.

Annual reports of two major operators in Norway for 2020 and 2021 also were considered. These consisted of short summaries that presented the main operational and economic results achieved by the company throughout the year. These reports were added to the evaluation to balance the higher technical content of the wellbore-history reports.