Reservoir Characterization and Modeling of Potash Mine Injection Wells in Saskatchewan
Bibliographic record
Abstract
In the Saskatchewan potash mining industry vast quantities of brine wastewater are generated from potash processing and mine inflow water. As treatment of such waste is prohibitively expensive, disposal in deep saline aquifers is the only readily available option. However, the effects that such injection activities have on the subsurface conditions of the targeted aquifers are not well known. Additionally, the reservoir characteristics of the targeted aquifers are not well understood. The aim of my dissertation is to provide a large scale reservoir characterization study to the aquifers used for subsurface waste disposal at the potash mines in Saskatchewan, the basal clastics and the Interlake Group. The second aim of this research is to use the data from the reservoir characterization study to build analytical models of the injection wells in order to study the effects that such injection activities have had on the targeted aquifers. Characterizing deep aquifers such as the basal clastics and the Interlake Group is often difficult due to limited subsurface information available at great depths. In this investigation, available information from oil and gas exploration and development, such as geophysical logs, drill stem tests and core analysis provided valuable information on the subsurface distribution and rock characteristics (permeability) of these formations. This information allowed for a better understanding of the factors that have led to the success of injection activities at the potash mine sites and will assist future projects targeting fluid injection in the basal clastics and the Interlake Group. Through this analysis, it was found that there are great differences in the permeabilities both spatially and with respect to lithology in these aquifers, with permeability values in the Winnipeg Formation between 10-18.1-10-11.8 m2 and 10-15.9-10-11.6 m2 in the Deadwood Formation and 10-16.0-10-11.5 in the Interlake Group obtained from the various methods of analysis. Through the information gained through the reservoir characterization study, analytical models were generated in Aqtesolv (HydroSOLVE Inc. 2016) in order to simulate the reservoir response to the injection activities. History-matching was conducted in order to generate models with simulation outputs that most closely matched the falloff test pressure data. The calibrated history-matched models were able to provide insights into the pressure response as well as the extent of the pressure propagations in the aquifers. It was found some of the potash mine sites have generated significantly higher pressure responses than others and that the injection rates and aquifer permeability played a significant role in the pressure response at each mine site.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".