Scale Inhibitor Squeeze Treatments Selection, Deployment and Monitoring in a Deepwater Gulf of Mexico Oilfield
Bibliographic record
Abstract
Abstract This paper describes field experience and lessons learned from scale-control operations in a deepwater development in the Gulf of Mexico. The injection of seawater for reservoir pressure maintenance resulted is a high barium-sulfate scaling tendency upon breakthrough of injection water which meant that it was critical to have effective scale management achieve high hydrocarbon recovery for this field. Use of conventional, water-based squeezes in the field did not deliver the squeeze lifetime expected and for some treatments resulted in production impairment. Hence, based upon risk mitigation, an alternative scale squeeze program was required this alternative program was supported by an extensive set of laboratory tests including reservoir condition coreflooding, based on these laboratory results it was decided to apply squeeze treatments of aqueous scale inhibitor proceeded by mutual solvent to reduce the risk of relative permeability damage and increase squeeze life by enhancing inhibitor adsorption. Aspects of well cleaning prior to scale squeeze deployment will be outlined as this has proved critical to the success of scale squeeze treatments in these wells which have an asphaltene tendency. Results from two of the five current scale squeeze treatments are presented to demonstrate that an integrated evaluation, treatment program design using squeeze design software, implementation and monitor (critical ions, inhibitor residuals, suspended solids) approach can being significant benefit to this challenging aspect of flow assurance. The paper will explore in detail the issues associated with inhibitor-squeeze treatment selection, deployment, monitoring in deepwater Gulf of Mexico and is a good example of implementation of best-practice sharing from another oil basin (Angola, West Africa) were the same operator overcome similar issues.
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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".