Supercritical water injection into unconventional reservoirs: A comprehensive flow model of an injection well connected to a reservoir
Bibliographic record
Abstract
Abstract Supercritical water injection (SCW) is a promising thermal method for enhancing oil recovery from hard‐to‐recover reservoirs. The success of this technique relies heavily on the downhole properties of the injected fluid, particularly under high‐pressure, high‐temperature conditions. In this study, a model of SCW flow through thermally insulated injection tubing, comprising both vertical and horizontal sections, is developed. An engineering code was created to simulate SCW injection, incorporating a simplified reservoir model in the horizontal section as a boundary condition, with reservoir injectivity determined iteratively based on wellhead pressure. Numerical examples illustrate the model's performance by examining the effects of varying injection system parameters. High‐precision correlations were employed to calculate fluid thermodynamic properties, enabling accurate simulations. Key factors such as initial SCW temperature, insulation thermal conductivity, and skin factor were analyzed to assess their impact on pressure, temperature, and specific enthalpy distributions along the tubing. The findings provide insights into optimizing SCW injection parameters, underscoring the potential of this approach for effective thermal recovery in unconventional reservoirs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".