Improved Health, Safety and Environmental Profile of a New Field Proven Stimulation Fluid
Bibliographic record
Abstract
Abstract Acidizing treatments can help significantly improve the productivity of a well. Safe handling of commonly used corrosive acids, however, is a significant challenge to properly manage during the execution of the treatments. The additives, such as corrosion inhibitors, iron control agents, etc., typically used to mitigate the corrosive impact of acids and other un-wanted reactions during the treatments adds to the complexity of safe handling, while further aggravating the environmental impact of the overall treatment. Higher dosages of additives that are needed at higher treatment temperatures and pressures further compound the challenge. With the industry needs trending to high temperatures & pressures, and stricter health, safety & environmental (HSE) considerations, an alternative stimulation fluid that is suited for all the challenging conditions and be globally applicable with an acceptable environmental profile would be ideal. Recent studies and field applications with a new stimulation fluid based on glutamic acid diacetic acid (GLDA) has shown that GLDA can improve permeability in carbonate and sandstone formations even at tough field conditions and can maintain the integrity of wells made of various tubular metallurgies. In the present paper the health, safety and environmental profile of GLDA is reviewed and compared against conventional stimulation fluids such as hydrochloric acid, acetic acid, hydrofluoric, etc., along with the commonly used additives. GLDA is to a large extent based on a natural amino acid made from sustainable resources. It is biodegradable in both fresh and seawater. GLDA has a very favorable eco-tox profile and very low toxicity levels. In contrast to other stimulation fluids and frequently used additives, GLDA does not have any hazard classification and therefore requires no adverse safety labeling. GLDA can be handled easily and safely in the field with just standard chemical handling precautions in place. Overall, GLDA is a safe, environmentally friendly and effective stimulation solution that can help improve productivity of a well with the least possible well integrity or HSE concerns.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".