Data-Driven Modeling and Uncertainty Quantification in Reservoir Production
A Special Issue of Processes (ISSN 2227-9717) belonging to the section "Petroleum and Low-Carbon Energy Process Engineering".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 180
Editor
Special Issue Information
Dear Colleagues,
Data-driven modeling approaches are increasingly ubiquitous in petroleum engineering, particularly with the emergence of sequence transduction models—commonly referred to as Large Language Models (LLMs)—which increasingly incorporate uncertainty. While these models represent a significant technological shift, comparative benchmarks—including data requirements, architectural complexity, interpretability, and the integration of physics and prior knowledge—suggest that traditional statistical approaches often maintain a competitive advantage, even if they lag in terms of prediction accuracy. This disparity is likely attributable to the relative novelty of sequence transduction models within the domain.
This Special Issue, titled “Data-Driven Modeling and Uncertainty Quantification in Reservoir Production,” invites original research that rigorously compares traditional data-driven methodologies with sequence transduction models. We seek contributions that evaluate these frameworks across the aforementioned benchmarks, as well as outstanding research focusing on the application of traditional, sequence-based, or hybridized approaches to data modeling and uncertainty quantification.
Recommended topics include, but are not limited to:
- Methodologies for incorporating uncertainty into both traditional and sequence transduction frameworks.
- Frameworks for embedding physical constraints and domain expertise into sequence transduction models.
- Advances in the interpretability and explainability of sequence transduction models.
- Hybridized approaches that leverage the strengths of both statistical and deep learning frameworks.
Dr. Obadare Awoleke
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- uncertainty quantification
- sequences transduction
- large language models
- deep learning
- model interpretability
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