Analysis of Steam/Solvent-Coinjection Processes by Use of Dynamic Gridding
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 169075, “A Methodological Analysis of the Mechanisms Associated With Steam/Solvent-Coinjection Processes by Use of Dynamic Gridding,” by A. Perez-Perez, M. Mujica, and I. Bogdanov, CHLOE, and J. Hy-Billiot, Total, prepared for the 2014 SPE Improved Oil Recovery Symposium, Tulsa, 12–16 April. The paper has not been peer reviewed. Hybrid steam/solvent processes have gained importance as a thermal-recovery process for heavy oils in recent years. Among the identified physical mechanisms that play a role during these processes are heat-transfer phenomena, gravity drainage and viscous flow, solvent mass transfer, and mass-diffusion/-dispersion phenomena. In this paper, a study of sensitivity to grid size is described. Ideally, this work will provide some insight into methodological aspects to be considered when hybrid steam/ solvent processes are modeled. Introduction Recent studies concerning the size of the liquid-solvent-rich zone where molecular diffusion and dispersion occur have implied that a detailed representation of the solvent/steam- chamber edge is necessary in the numerical model. To represent this front appropriately, the authors propose application of fine-grid models to represent the steam/ solvent condensation zone and, in parallel, activation of the adaptive-mesh refinement (AMR) option for amalgamating the internal and external portions of the steam chamber zone. It is expected that, with this simulation strategy, a detailed representation of the front will be caught by a fine model, while a coarse model will be sufficient to represent the heat-transfer phenomenon that dominates in the noncritical zone. For a discussion of the amalgamation process, please see the complete paper. Model Description Synthetic Reservoir Model. A synthetic reservoir representing a generic formation of Athabasca oil sands (western Canada) is used in this work. The reservoir consists of a pay zone rich in oil. The pay zone is 45.5 m in length (x-direction) and 50 m in thickness (z-direction).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".