Geochemical Characterization of Vaca Muerta Produced Water and Shallow Aquifers in the Neuquén Basin
Bibliographic record
Abstract
Abstract The objective of this study is to better understand the baseline aqueous chemistry and provenance of all groundwaters from the near surface shallow aquifers to the deeper Vaca Muerta shale ~ 2000 m depth subsea in the Neuquén Basin for water disposal monitoring. The regions included in this study are Cruz de Lorena, Coiron Amargo Sur Oeste and Sierras Blancas blocks that are operated by Shell Argentina. This integrated project utilized previous work on the detailed hydrogeologic characterization of aquifers from seismic, downhole petrophysical logs and pressures measurements, and adds further water sample geochemistry data and interpretations focused on water disposal monitoring. The baseline measured water ionic and isotopic compositions from 70 downhole and produced water samples from 8 vertical wells and 3 comingled processing facilities suggests that natural geochemical compositions can be used to distinguish potential mixtures of 1) shallow fresh potable water aquifers, 2) irrigation or industrial water sources, 3) industrial water disposal zones and 4) produced Vaca Muerta Formation water. 5% to 10% mixtures of injected Vaca Muerta produced water and the Centenario Formation (disposal zone) were reliably detected, plus Vaca Muerta produced water and the Rayoso Formation (monitor zone) with confidence. Some variability in formation water baseline compositions occur geographically within each formation, especially the Rayoso Formation that is mainly low salinity water less than 5000 mg/l but may naturally mix with Centenario brines in some areas. Overall, this baseline natural geochemical variability is interpreted to result from topographic driven meteoric water influx with subsequent groundwater mixing of potentially very old brines, mineral dissolution and rock water interactions. Interestingly, the Centenario water disposal zone is very saline many times that of sea water, and more saline than Vaca Muerta produced water. This high salinity in the Centenario Formation is interpreted to result from mixtures of meteoric water, paleo sea water and evaporitic brines. Salt and evaporite mineral dissolution are possible but no known formation has been identified as the source of the salinity. The Vaca Muerta shale produced water has a unique fingerprint similar to other shale flowback waters such as from the Duvernay Formation in Canada, both interpreted as mineral and clay bound water interactions that occur quickly with the frac make up water. A Python based algorithm called Pymix was utilized to test the end member mixing model sensitivity to assess potential cross formation groundwater mixing scenarios that could notionally occur due to water injection wells. Actual samples of Vaca Muerta and Rayoso water tested with a Pymix model came within 0% to 6.9% of their actual water composition end members (i.e. percent difference Pymix versus actual). In a second Pymix model, the actual samples of Vaca Muerta and Centenario water came within 0% to 10.2% of their actual water composition end members, with a larger uncertainty due to the more similar water ion and isotopic fingerprints of these more saline, deeper sourced groundwaters. This workflow is a good example of baseline aquifer data collection and interpretation for unconventional plays. This hydrogeologic and geochemical information can be used to perform water surveillance on water disposal and monitor wells, key for the development of the Vaca Muerta shale in Argentina.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".