Development of a Universal Ranking for Friction Reducer Performance
Bibliographic record
Abstract
Abstract In hydraulic fracturing, large amounts of water are pumped at high speed down the wellbore. To reduce pump pressure and costs, a friction reducer is added to the stream. There is currently no unified performance criteria for selection of friction reducers. This work outlines the methodology for producing such a unified method of comparing performance between any friction reducer chemical additives, both liquid and dry powder. A 0.5 inch stainless steel high-flow low-shear flow loop pumping at ranges between three and twenty gallons per minute was custom-built. The loop uses a Coriolis flow meter, two absolute pressure transducers, and one differential pressure transducer to accurately determine the friction reducer additive performance in any given fluid by measuring pressure drop across a section of developed flow. This paper utilizes over 400 in-house flow loop tests to establish a novel unified ranking system for the evaluation of friction reducers’ performance. The ranking is independent of the type of friction reducer used and quality of water. Produced waters, fresh water, treated produced waters, and synthetic waters were all used to validate the methodology and ranking system to create a unified criteria to compare performance of any friction reducers. Tomson Technologies created a standardized metric for assessing and ranking friction reducer performance. This standardization was achieved through the use of an unique performance scale comprised of the weighted average of the most important friction reduction parameters of a friction reducer in any produced water: (1) inversion time (InvT), (2) maximum percent friction reduction (Max% FR), (3) time to sustain maximum percent friction reduction (RetT@%Avg.FRmax), and (4) the percent friction reduction at the end of 500 seconds (% FR@500s). 500 seconds is used because fluid during hydraulic fractures travels from the pumps to the reservoir in 500 or fewer seconds in almost all cases. This scale is measured in a new unit called "Friction Reducer Units" (FRU), which ranges from 0 to 10. FRU has been used to rank and correlate the performance of different classes of friction reducers in various types of waters, resulting in a comprehensive results database and is used to show applicability of the overall metric.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".