A Green Alternative for Determination of Frac Height and Proppant Distribution
Bibliographic record
Abstract
Abstract As regulations and public awareness regarding hydraulic fracture operations continue to change, it is important to continue to look for options to reduce risk to both the environment and personnel. Current fracture diagnostic technology uses radioactive materials which can pose a high risk from a health, safety and environment (HSE) perspective. Exposures to people and the environment to radioactive chemicals; and the potential to cause pollution or long term detrimental health problems, are great. A technology has been developed to allow fracture diagnostics to be performed with zero-risk to the environment and personnel. The technology involves using Boron Carbide particles added to the frac slurry as a tag material. Boron Carbide is a ceramic compound that has a 75% abundance of Boron by weight and the same density as Silica. It is a compound that is chemically inert under typical conditions of hydraulic fracturing. Because Boron is a neutron absorber, post-frac detection is accomplished by using a neutron device utilizing an Am-241Be sealed source which detects descending neutron and gamma count rates, as well as, capture gamma validation by energy discrimination across tagged intervals. This method will give both near and not near well bore dimension and provides Neutron-Neutron (N-N) and Neutron-Gamma (N-G) differences against initial base line reference data. Field data and analysis of results are presented for a vertical coalbed methane well in Virginia as well as a horizontal Berea Sanstone well in Kentucky.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".