PETRA: Transforming Web Text for Petroleum-Engineering Domain Adaptation

Kirill Dubovikov; Omar El Mansouri; Hachem Madmoun; Yanda Li; Sandeep Kumar; Aya El Mir; Supriyo Ghosh; Writabrata Bhattacharya; Adrian Garcia-Garcia; Onkar Pandit; Sunil Kumar Sahu; Federico Castanedo; Larry Murray; Martin Takac; Salem Lahlou

arXiv · 2606.24346

PETRA: Transforming Web Text for Petroleum-Engineering Domain Adaptation

Kirill Dubovikov·Omar El Mansouri·Hachem Madmoun·Yanda Li·Sandeep Kumar·Aya El Mir·Supriyo Ghosh·Writabrata Bhattacharya·Adrian Garcia-Garcia·Onkar Pandit·Sunil Kumar Sahu·Federico Castanedo·Larry Murray·Martin Takac·Salem Lahlou

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Abstract

Petroleum-engineering search exposes a supervision gap for strong general retrievers: relevant evidence exists in public web text, but domain relevance labels are scarce. To address this gap, we propose PETRA, a large-scale Petroleum Engineering Text for Retrieval Adaptation dataset and pipeline that converts noisy public web data into a curated domain corpus and synthetic supervision for dense retrieval and reranking. PETRA contains 1.36M curated chunks, approximately 2B token equivalents, $\approx$859k, embedding training rows from $\approx$224k anchors, and roughly 400k teacher-scored reranker candidate rows. Its construction combines high-recall energy-domain curation, an energy-domain classifier with 98.4% test accuracy, chunk-grounded query generation, LLM-written hard negatives, and retrieval-mined candidate lists. PETRA improves first-stage in-domain Normalized Discounted Cumulative Gain (nDCG) from 0.703 to 0.763 through score fusion. Reranker adaptation improves the public Earth Science benchmark by 44% relative and a six-task reasoning-intensive panel by 23%. Failed training recipes show that high train-holdout accuracy on synthetic labels does not predict retrieval gains; retrieval-mined data helps only after being repackaged as teacher-scored candidate lists sampled from the inference-time candidate distribution.

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Kirill Dubovikov, Omar El Mansouri, Hachem Madmoun, Yanda Li, Sandeep Kumar, Aya El Mir, Supriyo Ghosh, Writabrata Bhattacharya, Adrian Garcia-Garcia, Onkar Pandit, Sunil Kumar Sahu, Federico Castanedo, Larry Murray, Martin Takac, Salem Lahlou. 2026-06-23. PETRA: Transforming Web Text for Petroleum-Engineering Domain Adaptation. https://arxiv.org/abs/2606.24346

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