Evaluation of Environmentally Friendly Chelating Agents for Applications in the Oil and Gas Industry
Bibliographic record
Abstract
Abstract For many decades, chelating agents have been used successfully as an additive in the oil and gas industry, for example during scale removal, iron control and matrix stimulation. More recently, these chemicals have also been used as standalone fluids for the same applications. However, the traditional chelating agents like ethylene diamine tetraacetic acid (EDTA), hydroxyethyl ethylene diamine triacetic acid (HEDTA) and nitrilo triacetic acid (NTA) suffer from slow biodegradability and/or an unfavorable health profile. To better meet the stricter health, safety and environmental requirements of the regulatory bodies and the industry, new environmentally friendly chelating agents have been introduced. The question is whether these new chelating agents have the required properties for a versatile downhole application. This paper compares four commercially available, readily biodegradable amino polycarboxylic acid type chelating agents, including glutamic acid N,N-diacetic acid (GLDA), aspartic acid N,N-diacetic acid (ASDA), methyl glycine diacetic acid (MGDA) and ethanoldiglycine (EDG) on a number of properties relevant for the oil and gas industry. It covers the solubility as a function of pH and in various acids, thermal stability, iron control, corrosion tests with low-carbon steel and Cr-based alloys and coreflood experiments on both carbonate and sandstone cores. The corrosion and coreflood experiments were conducted under realistic temperature and pressure conditions. Although the structural resemblance of the tested chelates is great, the results proof that even the slightest change in the chemical structure can have a significant impact on the properties and hence the use in the oil and gas industry. Furthermore, the results show that the new generation of chelating agents include candidates that have a lot more to offer than the traditional chelates in terms of corrosion, functionality in matrix acidizing jobs, descaling, impact on tubular, completion and environment. Our studies show that GLDA is the most versatile environmentally friendly chelating agent.
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 | 0.000 | 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".