Modelling of Enhanced Scale Control via Inhibition of Stimulation Fluids for Deepwater Developments
Bibliographic record
Abstract
Abstract The injection of seawater into oil bearing reservoirs 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 deepwater subsea developments offshore West Africa, Gulf of Mexico and Brazil have brought into sharp focus the need to manage scale in an effective way. In recent years there has been some consideration given to deployment of scale inhibitor within the fluids associated with the completion of production wells, prior to the start up of production. Until now, effective scale control in frac packed wells at low water cuts has been achieved with phosphonate-based inhibitors applied as part of the acid perforation wash and overflush stages, prior to the actual frac packing operation itself. The deployment of these inhibitors has proved effective in controlling barium sulphate scale formation during initial seawater production, and eliminating the need to scale squeeze the wells at low water cuts (<10% BS&W). Recent developments allowing inclusion of scale inhibitor in the linear and cross linked gel stages has highlighted the need to be able to model this process effectively, thereby enabling optimal use of the chemical and improved squeeze designs. This paper outlines simulation work carried out using the Petroleum Experts REVEAL software to assess introduction of scale inhibitor into frac pack operations, and identify the most suitable stage of the well completion process during which to apply the inhibitor, to maximise treatment life. Simulation results and field data from these treatments are compared to demonstrate the opportunity this technique presents, and to highlight the importance of chemical placement and the post stimulation flow regime to squeeze life.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".