Produced Flowback Salts vs. Induced-Fracture Interface: A Field and Laboratory Study
Bibliographic record
Abstract
Summary The water recovered from hydraulic-fracturing operations (i.e., flowback water) is highly saline, and can be analyzed for reservoir characterization. Past studies measured ion-concentration data during imbibition experiments to explain the production of saline flowback water. However, the reported laboratory data of ion concentration are approximately three orders of magnitude lower than those reported in the field. It has been hypothesized that the significant surface area created by hydraulic-fracturing operations is one of the primary reasons for the highly saline flowback water. In this study, we investigate shale/water interactions by measuring the mass of total ion produced (TIP) during water-imbibition experiments. We conduct two sets of imbibition experiments at low-temperature/low-pressure (LT/LP) and high-temperature and high-pressure (HT/HP) conditions. We study the effects of rock surface area (As), temperature, and pressure on TIP during imbibition experiments. Laboratory results indicate that pressure does not have a significant effect on TIP, whereas increasing As and temperature both increase TIP. We use the flowback-chemical data and the laboratory data of ion concentration to estimate the fracture surface area (Af) for two wells completed in the Horn River Basin (HRB), Canada. For both wells, the estimated Af values from LT/LP and HT/HP test results have similar orders of magnitude (approximately 5.0×106 m2) compared with those calculated from production and flowback rate-transient analysis (RTA) (approximately 106 m2). The proposed scaleup procedure can be used as an alternative approach for a quick estimation of Af using early-flowback chemical data.
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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 | 0.000 | 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".