An efficient numerical model for nonisothermal fluid flow through porous media
Bibliographic record
Abstract
Abstract Temperature transient analysis has gained increasing attention in recent years due to the widespread application of downhole temperature sensors with high resolution and accuracy. With the fast, high resolution, and accurate temperature sensors in oil and gas wells, there is a need for the models and methods for analysis of transient temperature data in order to identify production and well completion problems and calculation of reservoir properties. Although there are many proposed analytical and numerical models, a majority of models are not applicable in practice due to limitations in basic assumptions and computational time. A procedure is proposed for numerical simulation of transient variations of sandface pressure and temperature. The fluid flow and energy equations are decoupled and solved simultaneously and the dependency of all properties on pressure and temperature is treated by nested iteration loops. The solution was compared with some analytical and numerical methods and was found as a computationally efficient calculation method. The benefit of the proposed method is its versatility, simplicity, speed, and ease of implementation in a programming language which can be used in conjunction with well test analysis models to correct for the effect of temperature and also constrain the uncertainty of reservoir parameters. It can also be embedded in compositional and reactive models to widen their applicability and accuracy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".