# Industrial Engineering with Large Language Models: A case study of ChatGPT's performance on Oil & Gas problems

arXiv: https://arxiv.org/abs/2304.14354 · HTML: https://mlsanity.com/paper/2304.14354/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Comparative Analysis of CHATGPT and the evolution of language models](https://mlsanity.com/paper/2304.02468/) (2023)
- [No Code AI: Automatic generation of Function Block Diagrams from documentation and associated heuristic for context-aware ML algorithm training](https://mlsanity.com/paper/2304.04117/) (2023)

## Built on by (most cited first, 7 of 7)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [Lifelong Learning of Large Language Model based Agents: A Roadmap](https://mlsanity.com/paper/2501.07278/) (2025)
- [Domain Specific Benchmarks for Evaluating Multimodal Large Language Models](https://mlsanity.com/paper/2506.12958/) (2025)
- [Large Language Models in Ambulatory Devices for Home Health Diagnostics: A case study of Sickle Cell Anemia Management](https://mlsanity.com/paper/2305.03715/) (2023)
- [ChatGPT & Mechanical Engineering: Examining performance on the FE Mechanical Engineering and Undergraduate Exams](https://mlsanity.com/paper/2309.15866/) (2023)
- [TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data](https://mlsanity.com/paper/2605.00060/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.