Offshore Oil Well Intervention Applicability Reasoning Q&A Dataset
#applicability assessment #rule-based reasoning #evidence-based question answering #offshore oil well intervention #well maintenance assessment #oilfield operations management
- 5000
- 0.3GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-10-19
AI Analysis & Value Prop
Designed for offshore oil and gas production, this dataset provides question-answer samples about well conditions, candidate intervention criteria, operational prerequisites, and exclusion rules. Each sample asks the model to determine whether a candidate intervention meets the stated screening logic, explain the key reasoning, or identify information that is missing for a reliable decision. It supports logical reasoning evaluation, reasoning-focused supervised fine-tuning, and professional question-answering model development.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| input_context | string | The source text containing well facts, candidate intervention criteria, operational prerequisites, exclusion rules, and the question to be answered. |
| reasoning_basis | string | The key well facts, candidate criteria, prerequisites, or exclusion rules supporting the assessment, and how they relate logically. |
| missing_information | string | Information that could affect the assessment but is not provided or cannot be confirmed in the input. Enter none if no information is missing. |
| applicability_result | string | The result of assessing the candidate against the input conditions. Use only satisfied, not satisfied, or insufficient information. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What does the dataset contain?
- Samples focus on offshore oil well intervention and include well facts, candidate criteria, operational prerequisites, exclusion rules, and questions. Answer fields provide an applicability decision, its reasoning basis, and any missing information.
- How are the samples structured?
- The data is organized as question-answer text with four fields: input scenario and question, applicability result, reasoning basis, and missing information. Results use the labels "satisfied," "not satisfied," or "insufficient information," supporting training and evaluation of reasoning over conditions and rules.
- Which training and evaluation tasks does it support?
- The dataset can support reasoning-focused supervised fine-tuning, professional question-answering model development, and evaluation of candidate screening, evidence-based explanations, and missing-information detection. It is intended for model training and assisted analysis, not as a substitute for field engineering review or operational safety procedures.
- How can the data be obtained, and what are the licensing terms?
- Please refer to the dataset product page or provider's license terms for availability, delivery format, and permitted uses. Before use, confirm that the license covers the intended training, evaluation, or commercial application, and follow applicable industry standards and compliance requirements.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Offshore Oil Well Intervention Applicability Reasoning Q&A Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/9f0f490fa92193618ba010bdc1cf3078?dataset_scene_id=12},
urldate={2026-10-10},
keywords={offshore well intervention, oil and gas dataset, intervention applicability, logical reasoning Q&A, well operation screening, reasoning SFT, domain Q&A data, offshore oil and gas},
version={1.0}
}Using this in research? Please cite us.
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