The Tradeoff Between Surfactant Costs and Water Heating to Enhance Friction Reducer Performance
Bibliographic record
Abstract
Abstract In slick water fracture stimulations the standard treatment utilizes water with very few chemicals. In the Horn River Basin during the winter months the water from surface sources can be very cold. Besides the obvious problem of freezing, the low temperature of the frac source water causes serious problems with the effectiveness of friction reducers by increasing the inversion time (the time to maximum friction reduction). In low temperature, high rate conditions the maximum friction reduction may not be reached as the fluid may have travelled a considerable distance through surface equipment, and even down the casing, without the friction reducer being fully effective. This condition can increase pumping pressure, horsepower charges and surface equipment failures. It can also affect the ability to get to design rates for the frac resulting in undesirable conditions such as extending the time to get to rate, being unable to start sand scours or pressuring out early on in the frac treatment. To solve the problem there have been two key solutions employed: either the water can be heated to a temperature where friction reducer inversion time is reduced and therefore is more effective, or more friction reducer is added to the cold frac source water until the friction pressure is manageable. On a multiple frac campaign in the Horn River these two methods were tested against a novel chemical that increased the effectiveness of the friction reducer in cold water. The presentation will include the field test data and a cost analysis of implementing this on a job by job basis. In addition, the foaming and flow back characteristics of this chemical were tested at near in situ conditions to determine the potential for unplanned consequences. In addition, the technique is being considered for use in a system where the primary fluid is warm (~ 25°C) brackish produced water but the auxiliary fluid supply may be fresh cold water. The objective is to show the cost and benefit of the chemical solution compared to heating or increasing friction reducer loading. Field testing was required to determine if the chemical solution could be applied more cost-effectively then inline heating of the cold frac water supply at full frac rate. Field testing was also conducted to determine if cold frac source water could be efficiently friction reduced with no external heating using only friction reducer, or a combination of a friction reducer and a novel chemical to reduce inversion time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".