Integrated Risk Analysis for Scale Management in Deepwater Developments
Bibliographic record
Abstract
Abstract Due to the increased cost of scale management in subsea compared to platform or onshore fields, and because of the more limited opportunities for interventions, it is becoming increasingly important to carry out a risk analysis process for scale management as early as possible in the field development plan. This process involves identifying the potential scale risks, and analysing and comparing the options available for managing those risks. This paper discusses how this risk analysis process should be carried out, with a strong emphasis on the need to integrate all the available production chemistry and reservoir engineering data. To demonstrate this process, an example from a development complex, which lies in >400 m (>1300 ft) water depths offshore West Africa, is used. The process has involved the following steps: Analysis of available brine samples to identify maximum scaling potential. Laboratory testing of available scale inhibitors to identify chemistry best suited to this system. Study of analogue fields to identify scaling risks in these fields, and how these risks have been managed, with implications for fields currently being studied. Modification of full field reservoir simulation model to predict seawater breakthrough and duration of seawater production, to identify when, for how long, and using how much inhibitor the wells would require squeeze treatments to control scale. This process involves using flow profiles derived from the reservoir simulation model, and applying them in a near well squeeze simulator to predict treatment performance to minimum inhibitor concentration measured from laboratory studies. Well-by-well analysis of predicted seawater production profiles and total water production rates to identify potential for correct placement of inhibitor by bullhead treatments in zones at risk of scale deposition. Modification of reservoir model to study impact of in situ scale deposition on brine chemistry at the production wells, and revision of requirements for inhibitor squeeze treatments Economic analysis of options available for scale management, comparing sulphate reduction with inhibitor squeezing, based on treatments specifications identified above. The result of this process in the reservoirs in question, which have a moderate to severe scaling tendency, has been to demonstrate that inhibitor placement by bullheading would result in satisfactory placement for all wells. If the assumption is made that no scale deposition takes place in the reservoir, then sulphate reduction becomes a viable option, due to the requirement for regular treatments and relatively high chemical concentrations required. However, taking into account cation losses due to scale deposition deep within the reservoir, the requirements for chemical treatments reduces and squeezing becomes the preferred option. From the simulation models, differences between the reservoirs concerned in terms of the contribution aquifer waters make to scale control were identified, with some wells at much higher risk than others due to the volumes of potentially scaling brines that are expected to be produced. This paper clearly demonstrates that a cross discipline approach using reservoir engineering, production chemistry and completion engineering can lead to a more complete assessment of the scale risk and the correct economic selection of the control program.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".