Comparative Study of Flowback Analysis Using Polymer Concentrations and Fracturing Fluid Tracer Methods: A Field Study
Bibliographic record
Abstract
Abstract This paper compares flowback efficiencies using polymer concentration and frac fluid tracer methods. Results are presented for the flowback efficiency of each frac fluid segment using non-radioactive chemical frac tracers injected in a well as well as the results for the total flowback efficiencies using polymer concentration and frac fluid trace analysis methods. Two wells were fraced and traced with various chemical frac tracers. Upon commencing flowback, samples of produced aqueous solution were collected according to a pre-designed sampling schedule that lasted for 72 hours. Samples were analyzed for tracer, polymer, calcium, potassium, sodium, and chlorine concentrations. With the use of the mass balance technique, the total flowback volume and flowback efficiency for each fluid segment were calculated using the tracer method. In addition, total flowback and flowback efficiency were calculated using both polymer concentration and tracer methods. To better evaluate and compare the results of polymer concentration and frac fluid tracer analyses, dynamic fluid leakoff tests were conducted in a laboratory environment using both low and high permeability core samples. Detailed laboratory and field results are presented along with comparison of flowback results from both polymer concentration and frac fluid tracer methods.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".