Simulation Based Dimensionless Type Curves for Predicting Waterflood Recovery
Bibliographic record
Abstract
Abstract Predicting waterflood recovery with simulation based dimensionless performance curves has advantages over the more traditional approaches in certain applications. This paper discusses the advantages of the type curve approach, how the curves are created, and how they can be applied to predict pattern and full-field performance. This technique is suitable for screening/ranking projects, and is particularly helpful when sensitivity and uncertainty analyses are necessary to improve reservoir management decisions. The dimensionless type curve methodology can be applied to many types of fields. A case study of a large, waterflooded field is presented to show how the curves are created and how they can be applied. In this field, a study of the geology and stratigraphy indicated that reservoir continuity, permeability variance, and effects of faulting were the most important drivers of recovery efficiency. Simulations were performed on 45 datasets to describe waterflood performance over the range of variation. A spreadsheet program was created to predict recovery of any description, based on interpolations of the simulation results. The dimensionless curves can be used to compare actual well performance to predicted performance, to predict full-field performance by summing well curves, and/or as the basis of an integrated evaluation tool. Using correlations to predict recoveries allows for ease of sensitivity analyses, and ease of application by casual users in an organization. This paper should benefit anyone who struggles with the task of applying simulation-based knowledge to everyday decisions when optimizing waterflood recovery.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".