Production Enhancement for a Northern Mexico Field Well Resulting from Flowback Evaluations Using Chemical Frac Tracers: A Case History
Bibliographic record
Abstract
Proposal The chemical frac tracing technology is used to evaluate flowback and flowback efficiency. This unique technique utilizes a family of environmentally friendly and fracturing fluid compatible chemical compounds to trace segment-by-segment injection of fracturing fluids. These chemical compounds have unique chemical and physical characteristics that make them detectable at low concentrations of 50 ppt (parts per trillion). These tracers are mixed at a known concentration into individual frac fluid segments as the frac fluid is pumped downhole and into the formation. Upon flowback, samples are collected and analyzed for tracer detection. With the use of the mass balance technique the flowback and flowback efficiency for each fluid segment are calculated. These precise flowback and flowback efficiency calculations yield a more accurate assessment of fracture cleanup efficiency which in turn helps to unravel cleanup problems. This paper presents a case history whereby four different chemical frac tracers were injected into four fluid segments of a frac job. The flowback and flowback efficiency calculations revealed low recoveries of the injected fluid segments through 86 hours of flowback. Based on the flowback results, a recommendation was made to shut in the well for 24 hours. Upon re-opening the well to production, flowback samples were again collected to evaluate flowback enhancement as the result of reservoir pressure buildup and or gel-breaking during the shut-in period. The total flowback efficiency increased by 50% while daily oil production increased by 62%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".