Using Industry Standards as a Way to Predict Sand Performance and Approve Sand Deposits: Is there a Catch 22?
Bibliographic record
Abstract
Abstract The sands employed as fracturing proppant have been historically qualified for that purpose based upon their ability to meet quality standards described by the American Petroleum Institute (API, 1995) and more recently, by the International Organization for Standardization (ISO, 2006). Notable products meeting those standards include white sands from the Ottawa deposits in the north central United States, and the so-called brown sands from deposits in central or the "heart" of Texas. Until recent times, these "quality" sand deposits provided sufficient supplies for the ongoing needs. However, the unprecedented surge of hydraulic fracturing activities over the past few years has resulted in demand outpacing the supply for sands that meet these requirements. Consequently, many ‘new’ sand deposits are being evaluated for use in fracturing applications, but unfortunately, a great many of those when subjected to API / ISO standards fail to make the grade in one area or another. Interestingly, it is commonly similar criteria which are being failed including acid solubility, sphericity & roundness, crush strength, and particle distribution. Thus, one is given cause to question the relevance of some testing practices on ‘real world’ performance of sand in a fracturing treatment. It is this point which illustrates a "Catch 22". A sand can pass industry standards as a quality proppant, but it may not necessarily meet the performance, or conductivity, requirements of a reservoir. Yet, as this study demonstrates, a sand source that fails some of the standard testing parameters might still meet the flow capacity needs of a reservoir. One can better understand the role that industry standards play in predicting sand performance and in approving sand deposits through closer examination. So, first, a discussion of some of the individual quality standards and their influence will be shared. Secondly, empirical data will demonstrate relative impact on sand performance, via conductivity, when one or more of qualifying parameters fails to pass. Lastly, guidance will be provided for the use of sands and other proppants, which although technically are unacceptable per industry standards, may be perfectly acceptable as ‘fit-for-purpose’ proppants in at least some segment of the fracturing market.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".