Performance of Borate-Crosslinked Fracturing Fluid Compromised by Shear-Induced Phase Separation
Bibliographic record
Abstract
Abstract Shear history simulation has been a useful laboratory tool to determine the effect of tubing/casing shear on the rheology of fracturing fluids. High shear rates experienced in the wellbore can significantly impair the viscosity development of metallic crosslinked fluids. The effect of shear history on the performance of metallic crosslinked fluids has been demonstrated in past studies and led to the development of many of the delayed crosslinking fluids used today. Recently, more fracturing treatments are being performed at higher wellbore shear rates and more treatments are being conducted with delayed-crosslinking borate fluids. The borate-crosslinked fluids are generally thought to be more tolerant to shear history with the crosslinking able to "re-heal" following exposure to high shear rates. Therefore, shear history simulation is rarely, if ever, conducted during rheological evaluation of borate-crosslinked fluids. This paper examines the effect of shear history on the viscosity development of a borate-crosslinked fracturing fluid. The testing was initiated due to a series of "screen-outs" experienced with a common, delayed-crosslinking borate fluid. Testing indicated that the fluid formulation used in these treatments was not stable after exposure to the high shear experienced in the wellbore. The fluid exhibited characteristics of shear-induced phase separation resulting in significant viscosity degradation and excessive "rehealing" time. To address this problem, the fluid composition was carefully optimized to eliminate the shear-induced phase separation and accelerate viscosity development after the shear history simulation. The recommended fluid changes were implemented and resulted in successful treatments with no screen-out issues. This paper details the testing used to observe the shear-induced phase separation and recommendations for optimizing borate crosslinked fluids used in high shear environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".