PetroBench: A Benchmark for Large Language Models in Petroleum Engineering

Xiang Wang; Tingting Zhang; Sen Wang; Ying Wu; Heng Meng; Peng Zhou; Peng Li

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arXiv · 2605.28032

PetroBench: A Benchmark for Large Language Models in Petroleum Engineering

Xiang Wang·Tingting Zhang·Sen Wang·Ying Wu·Heng Meng·Peng Zhou·Peng Li

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Abstract

Large Language Models are increasingly applied in the petroleum industry, highlighting the need for a domain-specific evaluation framework. This study develops a benchmark for LLMs in petroleum engineering, including a three-stage process of data preprocessing, quality filtering, and multi-model validation. Using expert review, a standardized question bank with strong domain relevance and discriminative capability was constructed. The benchmark covers production, reservoir, and drilling engineering, with 1,200 questions across multiple-choice, true or false, term definition, and short-answer formats. Eight mainstream LLMs were evaluated under a unified API environment. Results show that models performed better on subjective than objective questions, indicating weaknesses in factual knowledge discrimination. The highest accuracies for multiple-choice and true or false questions were 65.3% and 74.3%, respectively. Gemini-3-Pro, Kimi-K2.5, and Claude-Opus-4.6-Thinking achieved the best overall scores of 72%-74%. Models performed best in production engineering and weakest in reservoir engineering. Chinese models showed advantages in multiple-choice questions, while international models performed slightly better in short-answer questions. The benchmark provides a reproducible and practical reference for evaluating and deploying LLMs in petroleum engineering.

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Xiang Wang, Tingting Zhang, Sen Wang, Ying Wu, Heng Meng, Peng Zhou, Peng Li. 2026-05-27. PetroBench: A Benchmark for Large Language Models in Petroleum Engineering. https://arxiv.org/abs/2605.28032

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