Appraisal of the Propping Potential of Luwa Sand in Nigeria for Hydraulic Fracturing Applications
Bibliographic record
Abstract
Natural sands made of spherical and round grains are widely used as proppants during hydraulic fracturing to increase recovery rate of hydrocarbon production. Synthetic proppants with high crush strength are employed for deep reservoir fracturing; however, this type of proppants suffer the disadvantages of high density, high cost and pose environmental hazards. This research was conducted with the aim to assess sands collected from Luwa River in Toro, Bauchi state of Nigeria for possible use as natural sand proppants. An epoxy resin-coated sand was produced using a simple method to modify the sands’ properties. A series of experiments were conducted in accordance with API recommended practice to determine the propping potential of the sand and the resin coated sand. The result from sieve analysis of 20/40 mesh size of Luwa sand revealed a mean size of 625.2 microns (0.625 mm). An X-ray fluorescence (XRF) result showed the presence of aluminosilicates (Al2O3 and SiO2) composition in Luwa sands. The sands recorded a hardness of 8 on the Mohs scale. Analysis of the shape parameters for sphericity and roundness was 0.8 and 0.8 (Krumbein-Sloss) respectively; it has bulk density of 1.64 g/cm3, acid solubility of 0.7% and 4.32% for the resin-coated sand. The sand has turbidity value of 28.98 NTU, loss on ignition in the range of 1.33% to 1.64% and crush resistance varies between 2000-3000 psi for Luwa sand and 4000-5000 psi for the coated sand. A comparison of the experimental results with API standard and conventional Ottawa and Brady models showed the sand competes favourably with these standards in all parameters for consideration as a proppant except for the crush resistance where more than 10% fragments was generated at low pressure of 3000 psi. However, the sand can be applied in shallow depth reservoirs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".