Revisiting Natural Proppants for Hydraulic Fracture Production Optimization
Bibliographic record
Abstract
Abstract Proppants are essential to the success of most hydraulic fractures and often account for the overwhelming cost of the treatment. Both the mass of proppant and the selection of the right type of proppant are essential elements in gaining the highest Net Present Value (NPV). It has been generally believed that in the lower closure stress environment (below 6,000 psi, i.e., shallow reservoirs), natural sands such as Brady and Ottawa are appropriate as proppants and, for the same mesh size, they provide essentially the same permeability. Commonly accepted notion is that manmade proppants (such as ceramics) should be applied at higher closure stress environment, invariably, deeper reservoirs. Three types of proppants are studied for a gas reservoir of Eagle Ford basin: Brady sand, Ottawa sand and ceramic. A fracture optimization p-3D model is used to maximize well performance by optimizing fracture geometry, including fracture half length, width and height. Reduced proppant pack permeability is compensated by larger width. Non-Darcy effects in the fracture are also considered. Post-treatment well performance is then estimated, using the optimized well geometry, leading to cumulative production over the well life. Finally, NPV analysis is employed as the criterion to select the best proppant for the job. In this project, we show there is an optimum Proppant Number corresponding to maximum NPV in various reservoir permeability. Based on that, we propose a systematic way of choosing proppant type and mass to maximize NPV in oil reservoirs. For tight gas reservoirs, we correct the prejudice that natural sand proppants cannot be applied to deeper reservoirs by showing NPV study results that are superior to those of manmade proppants. Keeping stimulation costs down, natural sands proppants have a much larger range of applicability than previously thought.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".