Opportunities for Implementing Cold Water for Fracturing Services in Western Siberia
Bibliographic record
Abstract
Abstract Hydraulic fracturing is one of the major techniques in modern well stimulation practices. The purpose of the current contribution is to gain novel insights on utilization of cold water for fracturing services in Western Siberia aiming to reduce non-productive time and price while maintaining excellent quality of service delivery. The study covers the hydration of non-modified guar gum and borate crosslinking in cold water conditions, the associated risks and plausible benefits are also considered. Among all fracturing services, conventional borate crosslinked guar gum fluids remain the most widely utilized due to their economical profitability, availability, ease of viscosifying and handling. The reduced temperature of water affects the guar swelling and hydration during linear gel preparation and influences the crosslinking reaction rate for delayed borate systems. One of the obvious drawbacks of the guar-based fracturing fluids is the necessity for a water heat-up process, especially during winter period. Within the scope of present study we are discussing the opportunities and perspectives of non-modified guar fluids for cold water fracturing applications. This original research details the comprehensive laboratory evaluation and thorough theoretical study, which presents a variety of fracturing fluids available to hydrate and crosslink in water temperatures starting from 5 degrees Celsius. It was revealed that the hydration of guar polymer (loading 3.6 kg/m3) in water varies between 82-88% at 5 degrees Celsius. The optimized borate-crosslinked fluids provide viscosity greater than 400 cP at 96 degrees Celsius. The versatility of proposed fracturing fluids was proven by exceptional viscosity recovery to 400 cP in less than one minute after high shear regime in the range of 10-50 degrees Celsius, simulating the fluid behavior in near-wellbore area at ambient temperature. The scope of work included the development of cold water implementation criteria and evaluation of possible associated risks, e.g. the additional cooling effect upon contact with proppant. The results presented in the current work pave the way for implementation of conventional borate-crosslinked guar gum fluids for cold water fracturing. Without significant price increase the proposed approach allows to decrease 30% of non-productive time and reduce heating expenses. The approach is significantly beneficial in areas exposed to cold winter conditions like Russia, Alaska or Canada.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".