Thermodynamic Prediction of Scale Formation in Oil Fields During Water Injection: Application of SPsim Program Through Utilizing Advanced Visual Basic Excel Tool
Bibliographic record
Abstract
This study focuses on the design and validation of a computer program named “SPsim”, developed using Visual Basic coding and advanced Excel tools to predict the formation of sulfate mineral deposits during water injection in oil fields. Water injection for secondary oil recovery is an effective economic strategy, but it can be negatively impacted by the formation of sulfate minerals such as calcium sulfate, gypsum, barium sulfate, and strontium sulfate. The results of this study demonstrate that SPsim can accurately predict the formation of these mineral deposits based on the composition of the formation water and injection water under various temperature and pressure conditions. Specifically, the formation of barium sulfate and calcium sulfate is observed under certain conditions, which is a significant concern in oil fields. The study also highlights that calcium sulfate, barium sulfate, and strontium sulfate are among the most challenging mineral deposits in the studied fields, while the formation of gypsum deposits is less significant. The program was compared with results from other software tools, such as ScaleChem 3.2 and StimCad 2, as well as field observations. The findings indicate that SPsim provides a reliable and effective tool for predicting and managing sulfate scaling in water injection operations, making it a valuable resource for both industrial and academic applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".