Asphaltene Precipitation Study during Natural Depletion at Reservoir Conditions
Bibliographic record
Abstract
Abstract This study concerns with experimental investigations of asphaltene precipitation at reservoir conditions of a crude sample from an Iranian oil reservoir during pressure depletion. High pressure high temperature set up and filtration method was used to quantify amount of precipitated asphaltene at different pressures. In addition the effect of temperature on asphaltene precipitation during pressure depletion has also been examined. The experimental results have been used to develop the asphaltene precipitation model with the application of commercial software as well. The movement of production systems to deepwater and subsea environments in recent years has increased the importance of fluid property related flow assurance issues. Fluid property variations that commonly occur during the production of oil, such as changes in pressure, temperature and composition, can precipitate asphaltenes. Asphaltene precipitation causes severe problems in reservoir oil production which is highly costly and laborious to remediate. Optimizing production in this case requires knowing the conditions under which asphaltenes will remain in solution. Experimental measurements at reservoir conditions play an important role in understanding asphaltene behavior as well as developing and justifying asphaltene models. The results of this study confirm that asphaltene precipitation increases when temperature decreases, this can be interpreted as a result of variation in volume fraction of species when temperature changes. After the pressure onset, asphaltene precipitation increases with reduction of pressure and reaches to a maximum near the bubblepoint. Furthermore reduction in pressure leads to asphaltene redissolusion due to light gas liberation. Also it is revealed that denser asphaltene components have tendency to precipitate initially. The results of this study can be applied as criteria for designing successful production operation in order to avoid asphaltene problem and also for thermodynamic model development or modification.
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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 | 0.000 |
| 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 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".