Flowback verses first-flush: new information on the geochemistry of produced water from mandatory reporting
Bibliographic record
Abstract
Unconventional oil and gas development uses the subsurface injection of large amounts of a variety of industrial chemicals, and there are concerns about the return of these chemical to the surface with water produced with oil and gas from stimulated wells. Produced water, including any flowback of injected fluids, must be managed so as to protect human health and the environment, and understanding the chemistry of produced water from stimulated wells is necessary to ensure the safe management of produced water. In 2014, California instituted mandatory reporting for all well stimulations, including sampling produced water two times and comprehensive chemical characterization of fluids injected and fluids recovered from stimulated wells. In this study, we analyzed data from mandatory reporting with the objective of closing previously identified data gaps concerning oil-field chemical practices and the nature of flowback and produced water from stimulated wells. It was found that the plug-flow conceptual model of flowback developed in shale formations, where salinity increases over time as produced water is extracted, was not appropriate for characterizing produced water from unconventional wells in these oil reservoirs, which are predominately diatomite and sandstones. In these formations stimulation caused a "first-flush" phenomena, where salts and metals were initially high and then decreased in concentration over time, as more produced water was extracted. Although widely applied to meet regulatory requirements, total carbohydrate measurement was not found to be a good chemical indicator of hydraulic fracturing flowback. Mandatory reporting closed data-gaps concerning chemical use, provided new information on acid treatments, and allowed more detailed analysis of hydraulic fracturing practices, including comparison of water use by geological formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".