Understanding the Origin of Flowback Salts: A Laboratory and Field Study
Bibliographic record
Abstract
Abstract Several past studies have focused on the saline flowback water to evaluate the hydraulic fracturing operations. The origin of the salts in the flowback water is important for the assessment of the flowback process. In this study, laboratory and field analyses are performed to provide a better understanding about the origin of the flowback salts. The field study analyzes the total salt concentration (salinity) and ion concentration data measured during the flowback process for the Muskwa (Mu), Otter-Park (OP), and Evie (Ev) formations. The concentration profiles of both the barium and chloride during the flowback process, whereas the iron concentration declines after experiencing an initial increase. The laboratory study encompasses contact angle, XRD, imbibition, individual ion concentration, surface element, and adsorption isotherm experiments for samples from the OP and Ev formations. To investigate the effects of fluid-rock interface area on the liquid uptake and diffusion rate of individual ions, a series of imbibition experiments are carried out for different values of surface to volume ratios (specific surface or "Asp"). The electrical conductivity and individual ion concentrations are measured during the imbibition process. XRD data is analyzed to determine the mineralogy of the samples. SEM-EDX analysis is performed to determine the distribution of the elements on fresh break and natural fracture surfaces of the samples (in addition to a sample from the Lower Keg (LK) formation). Finally, since both ion transfer and water adsorption processes occur during the imbibition experiment, an adsorption isotherm experiment is carried out to prevent the ion transfer into/out of the rock in order to solely study the water adsorption process. The laboratory results show that barium is mainly concentrated in the natural fractures; and therefore the shape of the barium concentration profile in the flowback water maybe an indication of the complexity of the fracture network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".