The Optimisation of a Scale Management and Monitoring Program for During the Production-Decline Phase of the Life Cycle
Bibliographic record
Abstract
Abstract This paper presents field results from scale squeeze treatments carried out on platform and subsea horizontal wells from a oilfield in the UK sector of the North Sea. Downhole scale control and the resulting squeeze treatments to production wells were highlighted as one of the most expensive items in the production chemical budget and impacted topside separation during treatment back production. The development of optimized scale squeeze treatments and monitoring policy has been critical to reducing the operating cost and deferred oil production of this asset as the produced water cut rose. Scale squeeze treatments have been optimised over the years with the aid of detailed reservoir simulation indicating water rates along the production wells being input into scale squeeze design software. The requirement to extend treatment life while minimising the deferred oil was one of the critical factors in selecting improved scale inhibitor chemistry. The field data from these wells will be presented comparing treatment lifetime rates between conventional treatments and the improved scale inhibitor chemistry. Evaluation of residual chemical concentration or scaling ion chemistry has long been used in monitoring programs. All these methods prove that the chemical is present in the brine when sampled or that scale formation is not occurring at the point of brine analysis. This paper outlines the experimental methods developed to evaluate the suspended solids collected from the produced brine by environmental scanning electron microscope (ESEM) and the associated brine chemistry to evaluate the scale risk within the produced fluids. The combination of these methods has improved the integrated scale management program in terms of evaluating scale squeeze placement effectiveness, squeeze lifetime and providing the confidence to extend the period between scale squeeze treatments and in some cases stop treatment where brine analysis alone would have suggested further scale squeeze applications.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".