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Record W2569637887 · doi:10.2118/184815-ms

Development of a Universal Ranking for Friction Reducer Performance

2017· article· en· W2569637887 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Hydraulic Fracturing Technology Conference and Exhibition · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsReducerPressure dropFriction lossRanking (information retrieval)TransducerMechanical engineeringComputer scienceProcess engineeringPetroleum engineeringMaterials scienceEngineeringMechanicsArtificial intelligencePhysicsElectrical engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.238
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it