Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
A Conceptual Framework for Automating Reservoir Characterization Workflows Using NLP and Data Integration
Lymmy Ogbidi, Benneth Oteh
31 December 2025 · Vol. 1, Issue 4, pp. 234-238
DOI: 10.65150/EP-gjetr/V1E4/2025-09
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What is this article about?
Reservoir characterization is a critical process in the energy industry, providing insights into subsurface properties essential for efficient exploration, production, and management. Traditional workflows are hindered by inefficiencies in handling diverse datasets, reliance on manual processes, and the challenges of integrating structured and unstructured information.
Article at a glance
- Published
- 31 December 2025
- Volume / issue / pages
- Vol. 1, Issue 4, pp. 234-238
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
Reservoir characterization is a critical process in the energy industry, providing insights into subsurface properties essential for efficient exploration, production, and management. Traditional workflows are hindered by inefficiencies in handling diverse datasets, reliance on manual processes, and the challenges of integrating structured and unstructured information. This paper proposes a conceptual framework to automate reservoir characterization workflows by leveraging advanced technologies such as Natural Language Processing (NLP), data integration tools, and visualization systems. The framework encompasses four key components: data acquisition and preprocessing, text analysis through NLP models, a robust integration layer for harmonizing datasets, and intuitive visualization tools for enhanced decision-making. The framework aims to improve efficiency, accuracy, and collaboration in reservoir studies by automating and streamlining these processes. The paper also discusses the anticipated benefits, including faster decision-making and greater insight, alongside challenges such as computational demands, data quality issues, and the need for domain-specific model customization. Strategies to mitigate these challenges and recommendations for future research and practical implementation are proposed. This framework represents a transformative approach, offering a scalable solution to modernize reservoir characterization and drive innovation in the energy sector.
Keywords: Reservoir Characterization, Automation, Natural Language Processing, Data Integration, Visualization Tools, Energy Industry
Cite this article
Lymmy Ogbidi, & Benneth Oteh (2025). A Conceptual Framework for Automating Reservoir Characterization Workflows Using NLP and Data Integration. Global Journal of Engineering and Technology Research, 1(4), 234-238. https://doi.org/10.65150/EP-gjetr/V1E4/2025-09
@article{Lymmy2025,
title = {A Conceptual Framework for Automating Reservoir Characterization Workflows Using NLP and Data Integration},
author = {Lymmy Ogbidi and Benneth Oteh},
journal = {Global Journal of Engineering and Technology Research},
year = {2025},
volume = {1},
number = {4},
pages = {234-238},
doi = {10.65150/EP-gjetr/V1E4/2025-09},
url = {https://doi.org/10.65150/EP-gjetr/V1E4/2025-09}
}Questions about this article
Who wrote this article?
Lymmy Ogbidi, Benneth Oteh.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 1, Issue 4, pages 234-238, published by EP Journals Group on 31 December 2025.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V1E4/2025-09 (https://doi.org/10.65150/EP-gjetr/V1E4/2025-09).
How do I cite this article?
APA: Lymmy Ogbidi, & Benneth Oteh (2025). A Conceptual Framework for Automating Reservoir Characterization Workflows Using NLP and Data Integration. Global Journal of Engineering and Technology Research, 1(4), 234-238. https://doi.org/10.65150/EP-gjetr/V1E4/2025-09
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