Interdisciplinary semantic model for managing the design of a steam-assisted gravity drainage tooling system
Bibliographic record
Abstract
Abstract Complex engineering systems often require extensive coordination between different expert areas in order to avoid costly design iterations and rework. Cyber-physics system (CPS) engineering methods could provide valuable insights to help model these interactions and optimize the design of such systems. In this work, steam assisted gravity drainage (SAGD), a complex oil extraction process that requires deep understanding of several physical-chemical phenomena, is examined whereby the complexities and interdependencies of the system are explored. Based on an established unified feature modeling scheme, a software modeling framework is proposed to manage the design process of the production tools used for SAGD oil extraction. Applying CPS methods to unify complex phenomenon and engineering models, the proposed CPS model combines effective simulation with embedded knowledge of completion tooling design in order to optimize reservoir performance. The system design is expressed using graphical diagrams of the unified modelling language (UML) convention. To demonstrate the capability of this system, a distributed research group is described, and their activities coordinated using the described CPS model. Highlights A modelling framework is proposed to manage interaction between engineering systems. Phenomenon feature concept is introduced to facilitate knowledge representation. Model framework is extensible and facilitates interoperability. Steam assisted gravity drainage oil extraction process is modelled.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".