Monitoring Fracturing Fluid Flowback with Chemical Tracers: A Field Case Study
Bibliographic record
Abstract
Abstract Proper fluid and proppant placement is the key to a successful fracture simulation. Performance of fracturing fluids, as they are pumped into a fracture, is the key to effective proppant placement. Fracturing fluids are used to both prop open a fracture and to effectively carry the proppants and place them into a fracture and to successfully flowback to the surface. Productivity of a well, after proppant placement, greatly depends on the flowback efficiency. This paper presents a novel technique where fracturing fluid flowback can be effectively monitored in a complex multi-zone fracture system. A family of environmentally safe chemical frac tracers, with unique characteristics, is developed to assure distinction of each zonal frac fluid upon flowback. The paper also presents a case where the technique was implemented in a multi-stage-fracturing job. Various chemical frac tracers were injected during different stages, which included pad fluid and crosslinked fluid with various proppant concentrations and breaker loading. Results and fracture flowback diagnoses are presented.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".