Stimulation of High Temperature SAGD Producer Wells Using a Novel Chelating Agent (GLDA) and Subsequent Geochemical Modeling Using PHREEQC
Bibliographic record
Abstract
Abstract Acidizing of sour, heavy oil, weakly consolidated sandstone formations under steam injection is a real challenge. Fines migration, sand production, inorganic scale, corrosion products, and damage due to asphaltene precipitation are some of the common concerns with these sandstone formations. They cause decline in the productivity of the wells, and there is always need to stimulate these wells to restore their productivity. Furthermore, the complexities of sandstone formations require a mixture of acids and several additives, especially at temperatures up to 360°F. Three treatments were tried in a horizontal well in this field: HCl acid, A (GLDA), and B chelating agents. In this paper, we evaluate the results of field applications using geochemical modelling, production data, and analysis of well flow back fluids after field treatments. The field treatment included pumping a foaming agent to have proper rheological characteristics and a better controlled pumping process, followed by the main stage of the treatments. The treatment fluids were displaced into the formation by pumping produced water and were allowed to soak for 6 hours, then the well was put on production, and samples of flowback fluids were collected. The concentrations of key cations were determined using ICP, and the chelate concentration of the chelating agent A was measured utilizing a titration method using ferric chloride solution. Geochemical modelling was conducted using specialized software, and was used to predict the concentrations of key ions in the flow back samples. The first two treatments including HCl acid and chelating agent B produced results below expectation. The third treatment using GLDA was successful and the well productivity increased significantly. The treatment was applied in the field without encountering any operational problems. A significant gain in oil production was achieved without adversely impacting the water cut, causing sand production, or fines migration. Analysis of flow back samples indicated that iron was the main cation, which shows that the chelate dissolved corrosion products. Geochemical modelling was able to predict the trend noted in the concentrations of key ions and chelant in the produced fluids.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".