Flowback Pattern-Recognition to Distinguish Between Formation and Fracturing Water Recovery
Bibliographic record
Abstract
Abstract We analyze flowback production data of 502 multi-fractured horizontal oil and gas wells completed in the Montney Formation and 83 oil wells completed in the Duvernay and Horn River Formations. The main goal of this paper is to evaluate the possibility of distinguishing between formation and fracturing water based on the water-flowback response. We hypothesize that: 1) the slope of water-flowback harmonic decline (HD) profile is reversely proportional to formation water mobility, 2) the deviations from the unit slope on rate-normalized pressure (RNP) plots is proportional to the slope of HD, and 3) the slope of water-flowback HD correlates with the initial water saturation (Swi). To verify our hypothesis, we 1) classify the observed HD trends of water-flowback rate based on slopes, 2) construct RNP diagnostic plots of the studied wells, 3) analyze log data and estimate average Swi by using Archie equation (1952) for the studied wells. 4) investigate the effects of Swi on the water-flowback pattern. The results show that there are two distinct flowback patterns. The first flowback pattern shows sharp slope (>10-41/day) of water-flowback HD profile and relatively high slope values (0.64 to 0.984 kpa/m3) of the corresponding RNP plots. However, the second pattern shows very low slope of HD (<5 × 10-5 1/day), with some wells showing no significant decline of water rate through the entire flowback process, also relatively low slope values (0 to 0.23) of the corresponding RNP plots. Analysis of the log data shows a positive correlation between Swi and slope of water-flowback HD profile. We also found that the slopes are proportional to the slope of RNP. These results indicate that as Swi increases, slope of HD decreases and there is more deviation from the unit-slope on the RNP plots.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".