Utilizing Fluid and Proppant Tracer Results to Analyze Multi-Fractured Well Flow Back in Shales: A Framework for Optimizing Fracture Design and Application
Bibliographic record
Abstract
Abstract Fluid and proppant tracers and other simple measurements in returning load water flow back can be very useful in helping to describe fracture development in shales; including such parameters as fracture complexity, frac conductivity, height growth, frac barrier effectiveness, well-to-well and frac-to-frac interference, water entry points and general fracturing execution. For most engineers, flow back ion charts have often had little relevance beyond estimating frac stage flow back activity; however, combining fluid tracer information with low-level gamma emitting proppant tracers, microseismic, simple salinity measurements and water return rate can help describe fracture and formation behaviors that lead to faster optimization of fracturing design and application. This paper will use fluid and proppant tracer results from over a hundred shale frac stages in horizontal wells along with other measurements of frac flow back and blend them with microseismic, frac pumping records, production logging and production results to build a framework for better analysis of frac flow back.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".