Control of Hydrogen Sulphide Scavenger Induced Scale and the Associated Challenge of Sulphide Scale Formation Within a North Sea High Temperature/High Salinity Fields Production Wells. Laboratory Evaluation to Field Application
Bibliographic record
Abstract
Abstract Precipitation of mineral scales causes many problems in oil and gas production operations: formation damage, production losses, increased workovers in both producers and injectors, poor injection water quality and equipment failures due to under-deposit corrosion. The most common mineral scales encountered with downhole and topside processes are sulphate and carbonate-based minerals. The development over the past few years of fields where high temperature and high salinity brines are being produced with associated hydrocarbon has presented a more challenging environment for scale management. In such fields hydrogen sulphide gas is quite a common component of the produced fluids. To remove the gas from the process stream hydrogen sulphide scavengers are applied. The introduction of these chemicals can result in a secondary problem arising from their relatively high pH. Changes in localized pH associated with the introduction of such production chemicals has been observed to induce carbonate scale precipitation and in the case of one high salinity brine system unexpected sulphide scales of lead and zinc. This paper will focus on the mechanism of occurrence of such scale types within a North Sea field and how an inhibitor package was developed that controls both carbonate and sulphide scale formation while it also effectively controls associated hydrogen sulphide. It will outline the field and laboratory testing methods utilized for assessment of the scale challenge, the chemical screening methods used and assess the currently available prediction software so that evaluation of the potential problem within fields during appraisal can be determined prior to production commencing.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".