Grain-scale analysis of proppant crushing and embedment using calibrated discrete element models
Bibliographic record
Abstract
Abstract Proppant crushing and embedment in hydraulically-induced fractures is a major drawback to the recovery of unconventional oil/gas and geothermal energy production. This study provides a grain-scale analysis of the fracture evolution mechanisms of proppant crushing, rock fracture damage during proppant embedment, the influence of realistic reservoir/fracture fluid on proppant embedment, and the behaviour of proppant packs subjected to in-situ stresses using a discrete element modelling (DEM) approach. The results of this study reveal that the selection of an appropriate proppant type based on the nature of the reservoir formation plays a vital part in quantifying the degree of proppant crushing and embedment within fractures. The utilisation of frac-sand proppants instead of ceramic proppants in shallow soft sedimentary-based siltstone formations reduces proppant embedment up to 88%. However, whatever the depth of the fracture, the injection of ceramic proppants into granite-based geothermal formations is preferred to that of frac-sand proppants due to their lower proppant embedment and greater crush resistance. DEM analysis detected rock-spalling during the proppant embedment process, which ultimately led to the initiation of tensile-dominant secondary fractures in rocks. Fracture initiation, propagation, and coalescence during proppant crushing are analysed using calibrated DEM proppant-rock assemblies. Importantly, this study reveals that the saturation of formation rocks with fracturing/reservoir fluids may cause a significant increase in proppant embedment. Furthermore, proppant crushing, embedment, and re-arrangement mechanisms in proppant packs with different proppant distributions are analysed in this comprehensive numerical study.
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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 | 0.001 |
| 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".