The Sanding Mechanisms of Water Injectors and their Quantification in Terms of Sand Production: Example of the Buzzard Field (UKCS)
Bibliographic record
Abstract
Abstract The sanding of water injectors is considered a serious issue, as it can trigger important injectivity reductions and may sometimes and may sometimes lead to the collapse of wells. Cross-flow during shut-in and the water hammer pressure wave generated by the well closure are recognized as the main contributing factors to sanding. However the paper will show that the precise mechanisms of sanding on water injectors have not been fully described yet. The unexpected and early collapse of an injector on the Buzzard field - i.e. the largest current oil producer in the UK - required the evaluation of the sanding risk for the other 12 injection wells, as the loss of another one would have been critical in terms of field management. The complete records of all the wells, including their injection histories were therefore recovered and analysed. The analysis revealed that four sanding mechanisms were at play: Natural cross-flow between layers at pressure equilibrium, Forced cross-flow between layers not at pressure equilibrium, Swabbing from the water-hammer pressure-wave, Surface-flow between wells. The occurrence of each mechanism for each well was checked and quantified through field data analysis, modeling and direct downhole measurements (injection logs and video). In particular, the amount of sand produced by each mechanism was quantified. The analysis showed that the forced cross-flow on the collapsed well had produced sand quantities orders of magnitude larger than what was experienced by the other wells and the risk of losing another well was therefore judged minimum. In addition measures were taken to limit the impact of all four mechanisms on the existing wells and a methodology was devised to avoid the conditions of the collapsed well on future wells. The paper presents a complete methodology for quantifying the risk associated with the sanding of injectors. In addition the measures taken to limit the impact of the various sanding mechanisms can easily be implemented without significant costs to all wells injecting in weak reservoirs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".