Removal of Divalent Cations from Produced Water and Its Impact on Rheological Properties and Proppant Settling Velocity
Bibliographic record
Abstract
The petroleum industry seeks to optimize the reuse of flowback and produced water (FPW) in hydraulic fracturing to reduce environmental impacts and costs. This study investigates how controlling divalent cations in FPW influences its rheological properties and proppant carrying capacity, both of which are crucial for efficient fracturing. Synthetic FPW, modified to simulate treated and untreated conditions, was analyzed to determine the impact of gel-based additives such as anionic polyacrylamide-based friction reducers (FRs). Results indicate that removing divalent cations increases relaxation times from 0.12 s in untreated FPW to 1.00 s in a 1 gallon per thousand gallons (gpt) FR solution, demonstrating improved viscoelastic gel characteristics. However, these changes do not significantly increase proppant carrying capacity. Even with relaxation times increasing to 4.5 s at higher FR dosages (3 gpt), the treated FPW still does not achieve the relaxation time observed in FR solutions using deionized (DI) water, which remain above 10 s. The removal of divalent cations from FPW resulted in only minor changes to its shear viscosity, with a modest 15% increase that was not enough to significantly affect the settling velocity of the proppant. Thus, removal of divalent cations can positively influence rheological behavior; it does not necessarily improve proppant transport efficiency in hydraulic fracturing operations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".