Multiphase Fluid Flow and Reaction in Heterogeneous Porous Media for Enhanced Heavy Oil Production
Bibliographic record
Abstract
This chapter focuses on the transport phenomena in pure steam, hybrid solvent-steam, and gas-solvent-steam co-injection processes, and the effects of reservoir heterogeneity on recovery performance. For the multiple thermal fluids (MTFs) injection process in heavy oil reservoirs, the reaction between thermal fluids and heavy crude oil will dominate the flowing process of MTFs in a reservoir. The energy equation for fluid flow through a porous medium can be derived by using the first law of thermodynamics. The chapter then discusses the dynamic heat and mass transfer in several heavy oil recovery processes, such as steam-assisted gravity drainage (SAGD), hybrid steam-solvent, and steam-solvent-gas co-injection. In a SAGD process, many different mechanisms are involved and they affect the drainage rate to varying degrees. The chapter further analyses the respective contribution of conduction and convection to the viscosity reduction of heavy oil and investigates effects of heterogeneous reservoir properties.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".