Shear Sensitivity of Borate Fracturing Fluids
Bibliographic record
Abstract
Abstract Borate-crosslinked fracturing fluids have been used in the oil and gas industry for nearly 40 years. These fluids consist of three basic components (polymer, crosslinker, and pH buffer) which are considered relatively simple to optimize for a variety of field applications. Among the unique features of this fluid is the ability of the crosslink viscosity to "re-heal" or "recover" after exposure to high shear rates. Based on laboratory tests described in SPE 134266, it was determined that the "re-healing" time can be excessive for some borate-crosslinked fluids exposed to high shear, resulting in limited near-wellbore viscosity and screen-outs in the field. Using a laboratory-scale flow loop to simulate the wellbore shear environment, various borate-crosslinked fluids were exposed to a wide range of shear history conditions before loading into a high temperature, high pressure rheometer. In addition to the common viscosity versus time profile, the early-time viscosity development of each fluid was analyzed to quantify the effect of shear history on recovery time. This paper defines critical shear rates and exposure times that adversely impact early-time viscosity development of borate-crosslinked fracturing fluids. Also, the test results show that the impact of wellbore shear conditions on recovery time can be minimized by adjusting the concentration of the polymer, borate crosslinker, and/or pH buffer. The techniques and guidelines provided in this paper can be used to identify detrimental wellbore shear conditions that will lead to excessive recovery times. The paper also demonstrates optimizing borate-crosslinked fluids with common on-site tests can result in fluid compositions with increased shear sensitivity.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".