Pre-Production Deployed Scale Inhibition Treatments in Deep-Water, West Africa
Bibliographic record
Abstract
Abstract The injection of seawater into oil-bearing reservoirs in order to maintain reservoir pressure and improve secondary recovery is a well-established, mature operation. Moreover, the degree of risk posed by deposition of mineral scales (carbonate/sulphate) to the injection and production wells during such operations has been much studied. The current deep-water subsea developments offshore West Africa and Brazil have brought into sharp focus the need to manage scale in an effective way. In a deepwater West African field the relatively small number of high-cost, highly productive wells, coupled with a high barium sulphate scaling tendency upon breakthrough of injection seawater meant not only was effective scale management critical to achieve high hydrocarbon recovery, but even wells at low water cuts have proven to be at sufficient risk to require early squeeze application. To provide effective scale control in these wells at low water cuts, phosphonate-based inhibitors were applied as part of the acid perforation wash and overflush stages prior to frac packing operations. The deployment of this inhibitor proved effective in controlling barium sulphate scale formation during initial water production eliminating the need to scale squeeze the wells at low water cuts (<10% BS&W). To increase the volumes of scale inhibitor being deployed in the pre-production treatments and so extend the treatment lifetimes scale inhibitor was also added to the frac gel used to carry the frac sand. This paper outlines the selection methods for the inhibitor chemical for application in frac fluids in terms of rheology, retention/release, formation damage and presents the chemical returns profile from the 5 wells treated (some treatments lasting > 300 days) along with monitoring methods utilized to confirm scale control in the wells treated. Many similar fields are currently being developed in the Campos basin, Gulf of Mexico, and West Africa, and this paper is a good example of best-practice sharing from another oil basin.
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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) | 0.001 | 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 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".