An effective approach to implement asphaltene precipitation in reservoir simulation
Bibliographic record
Abstract
Asphaltene precipitation and deposition in rock surfaces in oil reservoirs causes formation damage and leads to low well productivity. Therefore, analyzing the asphaltene behavior in terms of precipitation, deposition and its impact on reservoir properties is vital for predicting the production performance of asphaltic oil reservoir. Pure solid model has been widely employed to predict asphaltene precipitation. However, this approach requires a series of flash calculations through a trial-and-error procedure. This leads to higher computational time. Therefore, in this work, a modification in pure solid model is introduced so that asphaltene precipitation can be estimated explicitly in a single-stage flash calculation in each time step, resulting in reduction of computational time. At a sample pressure 2800 psia, asphaltene precipitation calculation has been presented by applying both pure and modified solid model. The comparative results show a difference of around 10% between these two methods. This work also discusses the simulation of wellbore region of production well in an asphaltic oil reservoir that experiences asphaltene precipitation and deposition. An analysis regarding development of asphaltene precipitation and its consequence on reservoir properties (porosity, permeability) and pressure is presented. For simulation, the black oil model of four phases (water-oil-gas-asphaltene) is developed and a detailed description on mathematical model development is presented. The result from the simulation shows that around the wellbore, the asphaltene precipitation is maximum resulting to maximum damage in permeability and porosity. In addition, comparing between “asphaltene precipitation” and “no asphaltene precipitation” cases, a difference of 11.2% in cumulative oil production is observed. • A new modification in solid model for explicit estimation of asphaltene precipitation. • A small difference between estimations by modified model and experimental data. • A faster modified method with less steps. • Solid molar volume of asphaltene with the most impact on asphaltene precipitation. • Total asphaltene content in asphaltene precipitation with the minimal impact.
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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 | 0.001 |
| 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".