Comparative Study of Different Sand Samples and Potential for Hydraulic-Fracturing Applications
Bibliographic record
Abstract
Abstract An unprecedented surge of hydraulic fracturing activities over the past few years has resulted in a huge demand for quality sand and consequently has increased the supply-demand gap for sand that meets quality standards. Sand used for hydraulic fracturing is qualified based on its ability to meet quality standards described by the American Petroleum Institute (API) and, more recently, by the International Organization for Standardization (ISO). White sand from the Ottawa deposits of the northern US and brown sand from deposits in the southwest US are widely used for hydraulic fracturing. Until recently, these "quality" sand deposits provided sufficient supplies for ongoing needs, but now there is a need to evaluate alternative sources of sand that meets the specifications given by the ISO. In this paper, performance of different sand samples procured from different parts of India and Saudi Arabia are discussed. The sand is characterized by performing sieve analysis, characterizing particle shape (i.e., sphericity and roundness), acid solubility tests, crush-resistance tests, and baseline conductivity experiments, for which the results are presented. Ottawa sand generated 7.7% fines, whereas one of the alternatives generated 8.1% fines at 4,000 psi. Similarly, conductivity for Ottawa and an alternative sand sample was 4,850 and 4,770 md-ft respectively at closure stress of 2000 psi. In conclusion, the results show less than 2% variation between Ottawa and the best alternative sand sample. Considering stringent ISO requirements, few alternatives for widely used Ottawa sand have been identified. Analysis performed in this study will help in evaluating locally available sand, which should provide more security of supply and economic benefits.
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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".