Mineral precipitation prediction and prevention during geothermal brine production in Clarke Lake field in British Columbia
Bibliographic record
Abstract
,Mineral precipitation is a common issue encountered in geothermal power facilities. Such scale formation is typically a result of variations in the geothermal fluid such as temperature, pressure, and ion composition. The precipitation of minerals can impede fluid flow leading to diminished plant efficiency and escalated maintenance expenses. The thesis aims to investigate brine production in the Clarke Lake field in British Columbia for geothermal purposes, focusing on studying scaling phenomena. Additionally, the study explores the integration of completion flowback water as a sustainable water resource for geothermal wells in the Clarke Lake field. The primary objective is to identify the types of scale that might form when flowback water is used in the geothermal system. Using a geochemical numerical tool PHREEQC (pH-REdox-EQuilibrium), the potential scale formation resulting from the mixing of formation water and two type of flowback waters are predicted. Through comprehensive laboratory experiments and analyses such as static bottle tests, scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS or EDX), X-Ray diffraction analysis (XRD), and inductively coupled plasma-optical emission spectrometry (ICP-OES), an in-depth insight into the scaling tendencies and behaviors of different minerals are gained. In the final stage of the thesis, the efficiency of a chemical inhibitor (Diethylene Triamine Penta) in preventing scaling was evaluated. Chemical inhibitors reduce the formation and growth of scaling deposits. Through rigorous testing, an assessment of the inhibitor's effectiveness in preventing scaling was conducted at both room temperature and elevated temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".