Introducing a mathematical workflow for scaling‐up the cyclic solvent injection process: Experimental studies and dimensional analysis utilizing Buckingham pi theorem
Bibliographic record
Abstract
Abstract Cyclic solvent injection (CSI) is an effective enhanced oil recovery (EOR) technique with several economic and environmental benefits. However, the successful implementation of CSI at commercial scales requires reliable scaling criteria. In this study, CSI has been formulated comprehensively by integrating material balance, mass transfer, and pseudo‐chemical reaction equations to derive key dimensionless scaling terms based on the Buckingham π theorem. Accordingly, a total of 11 dimensionless terms have been identified, which encompass a wide range of phenomena, including foamy oil mobility and its intricate dynamics, as well as solvent exsolution processes. In addition, to account for pressure propagation delay in larger reservoirs, an effective workflow was established to systematically modify the permeability in lab settings in such a manner that its results can be translatable into larger models. Furthermore, two sandpack models were employed to perform CSI experiments. These models were subsequently scaled up into two synthetic reservoirs, constructed using the CMG software package, by applying the proposed scaling methodology to validate the proposed scaling approach. The findings indicate a reasonable match in terms of recovery factor, cumulative gas production per unit pore volume, and cumulative gas–oil ratio (CGOR) versus dimensionless time between the synthetic reservoirs and the sandpack models. The proposed scaling workflow offers a foundation for future research on scaling methodologies in solvent‐based heavy oil recovery processes. Additionally, it can be used to optimize recovery strategies and reservoir management by enabling more accurate predictions of CSI performance at larger scales.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".