Stimulation of High-Temperature Steam-Assisted-Gravity-Drainage Production Wells Using a New Chelating Agent (GLDA) and Subsequent Geochemical Modeling Using PHREEQC
Bibliographic record
Abstract
Summary The acidizing of sour, heavy-oil, weakly consolidated sandstone formations under steam injection is challenging because of fines migration, sand production, inorganic-scale formation, corrosion issues, and damage caused by asphaltene precipitation associated with these sandstone formations. These and other similar problems cause decline in the productivity of the wells, and there is a recurring need to stimulate them to restore productivity. The complexity of sandstone formations requires a mixture of acids and several additives, especially at temperatures up to 360°F, to accomplish successful stimulation. Three treatments were tested on a horizontal well in the field: hydrochloric acid (HCl); Chelating Agent B, a high-pH chelant; and Chelating Agent A, or glutamic acid Ν,Ν-diacetic acid (GLDA). The first two treatments with 15 wt% HCl and high-pH (pH = 10) Chelating Agent B produced results below expectations. The third treatment using GLDA was successful, and the well productivity increased significantly. The field treatment with GLDA included pumping the treatment fluid, which was foamed to create proper rheological characteristics and a better-controlled pumping process. The treatment fluids were displaced into the formation by pumping produced water and were allowed to soak for 6 hours. In this paper, we evaluate the field applications of GLDA using geochemical modeling, production data, and analysis of well-flowback fluids after the field treatments.
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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) | 0.001 | 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".