Post-frac Flowback Water Chemistry Matching in a Shale Development
Bibliographic record
Abstract
Abstract Shale developments are normally hydraulic fractured to stimulate the low permeability of the reservoirs, in order to allow fluid to flow to the wellbore. The most common fluid fracture deployed in shale developments is slickwater; which is typically composed volumetrically of approximately 95% water, 4% proppant and 1% other chemicals such as scale inhibitor, surfactant, biocide and corrosion inhibitor. Water management in shale plays accounts for 5% - 15% of total well completion costs. This study investigates the fate of fracturing fluids in shale developments and attempts to understand the effect of fracturing fluid trapped within the reservoir. Approximately 5% - 50% of fracturing fluid pumped is flowed back as the well is put on production. Scale deposition is often experienced within these wells due to the interaction of fracturing fluid lost to the formation reacting with formation brines. It is estimated that the formation of scale within the reservoir, blocks of nano-pores and reduces to some extent the fraction of fracturing fluid returned. The main purpose of this study was to simulate the post-frac flowback composition using a reactive transport model. The model simulates the injection of the fracture fluid, when in contact with the reservoirs minerals, a number of geochemical processes take place and with subsequent production further reactions are possible. The model was used to evaluate the possible causes of the high TDS content in the post-frac water, on one hand dissolution of salts present in the shale or the breaching of deep saline aquifers during fracturing. The value of this paper being to the industry is to increase the understanding of the geochemical reactions occurring during shale fracturing which will impact produced water reuse, scale inhibitor selection to prevent inorganic scale deposition resulting in better fracture performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".