Determining Reservoir Properties and Flood Performance from Tracer Test Analysis
Bibliographic record
Abstract
Abstract In the last several years a variety of new tools for interpreting interwell tracer tests have been developed. The new methods are based on residence time distributions of the tracer, where much of the previous work used only the mean residence time. Using the distribution of residence times extends the power of moment analysis by allowing for the determination of reservoir properties and flood performance as a function of time. Flow geometry and construction of flow capacity - storage capacity diagrams also follows directly from the analysis. Swept volume vs. time, and sweep efficiency are also determined from the residence time distribution, as is remaining oil saturation. One important key to these new methods is our use of the integrated tracer recovery histories. Estimating residual oil saturation is greatly simplified by our mathematical treatment of slug tracer injection. Examples are presented that show improved saturation estimates even at early times in a tracer test. This paper describes the new analysis methods developed recently and shows by comparisons with analytical and experimental data that the methods are accurate and robust. The method is simple and can be done with a spreadsheet using only produced tracer concentration data; it does not require a reservoir model or numerical simulation. The equations are derived from first principles for a very general case that includes both conservative and partitioning tracers produced from any heterogeneous reservoir.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".