Experimental study on mineral precipitation prediction and mitigation for geothermal fluids in Clarke Lake Field in British Columbia, Canada
Bibliographic record
Abstract
• Primary ions in Clarke Lake Field fluids are Ca 2+ , Mg 2+ , Na + , Cl - , SO 4 2- , and HCO 3 - . • Major scaling minerals predicted in Clarke Lake Field are CaCO 3 and CaMg(CO₃)₂. • Using flowback water could introduce CaCO 3 , CaMg(CO₃)₂, and BaSO 4 scaling. • Inhibitor efficiency against scaling in formation fluids is higher than flowback water. Mineral precipitation in geothermal facilities, caused by variations in geothermal fluid temperature, pressure, and ion composition, impedes fluid flow and reduces energy utilization efficiency. This study investigates the potential mineral precipitation of geothermal fluids in the Clarke Lake Field in British Columbia, focusing on unveiling scaling phenomena and exploring the potential integration of hydraulic fracturing flowback water. The primary goal is to identify potential scale types in geothermal brines from various fluid sources (i.e. formation water and two types of flowback water) and evaluate scale mitigation using a commonly used inhibitor. Geochemical simulations using PHREEQC were performed to analyze the influence of temperature and water chemistry on the formation of precipitation. Subsequently, laboratory experiments and analyses were performed to confirm mineral precipitation and evaluate inhibitor performance, including static bottle tests, scanning electron microscopy, X-ray diffraction, and inductively coupled plasma-optical emission spectrometry. Both simulations and laboratory analysis identified dolomite (CaMg(CO₃)₂) and calcite (CaCO₃) scaling for flowback water with additional barite (BaSO₄) scaling for some water samples, while formation water showed CaCO₃ and CaMg(CO₃)₂ in the Clarke Lake Field. The study also demonstrates the efficacy of the inhibitor in mitigating scale formation by inhibiting nucleation and chelating calcium and barium ions, with an inhibition efficiency of 63.2 % for CaCO 3 in formation water and efficiencies of about 30 % for BaSO 4 and CaCO 3 in flowback water.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".