Changing the Injection Water on the Blane Field, North Sea: A Novel Approach to Predicting the Effect on the Produced Water BaSO4 Scaling Risk
Bibliographic record
Abstract
Abstract The Blane Field, North Sea, has one injection well and two production wells and is tied back to the Ula Field platform. The original scaling risk assessment was based on injection of platform produced water (PW) with minor seawater (SW) (~90:10 PW:SW). However, after injection of 25:75 PW:SW for only 6 weeks, a change in operational circumstances on the Ula Field meant that only 10:90 PW:SW injection water could be supplied for the next 18 months. There was a risk that this might result in unmanageable BaSO4 scaling conditions in the production wells but the alternative would be to cease injection, leading to reservoir pressure decline and loss of oil revenues. The need for a rapid decision negated the use of reactive transport reservoir simulations to predict the future BaSO4 scaling risk under the new injection scenario so a novel, alternative approach was adopted. A history matched ECLIPSE model served as the basis for predicting the types of water entering the production wells over time and their rates. A 1-D reactive transport model was then used to predict the Cl, Ba and SO4 composition of these waters after accounting for the effects of reservoir reactions. These results were integrated in a spreadsheet to provide predictions of Cl, Ba and SO4 concentrations in the produced water from each well over time. The results for future injection water scenarios indicated that the scaling risk would increase over time in the wells but, due to deposition of BaSO4 and CaSO4 in the reservoir, the BaSO4 scaling risk would be manageable even allowing for uncertainties associated with this approach. Based on these results, and those of associated studies, a decision was made to continue water injection resulting in avoidance of loss in oil revenues. This novel scaling prediction approach may be useful on other fields where reactive transport reservoir simulations may not be possible.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".