Finite Volume Method for Solving a Modified 3-D 3-Phase Black-Oil Hydrocarbon Secondary Migration Model, and Its Application to the Kuqa Depression of the Tarim Basin in Western China
Bibliographic record
Abstract
By using a finite volume method as a solver, a modified 3-D 3-phase (water, oil, gas) black-oil model for modeling hydrocarbon (HC) secondary migration in the context of basin modeling is presented in this paper. The model predicts the quantity and distribution of HC accumulation in space and time. The black-oil model used in basin modeling is more complex and more difficult to model than that in reservoir simulations, as the model includes variable simulation ranges, very long simulation times, initial conditions, natural sources and sinks, and reservoir gridcells. In the proposed finite volume formulation, the gridding of variable 3-D geological volumes is performed using perpendicular bisection (PEBI) gridcells, which makes the discretization and subsequent implementation of 3-phase flow equations much easier than when using hexahedral or tetrahedral gridcells. The stability and convergence of the solutions have been improved by using finite volumes with PEBI gridcells and the fully implicit formulation. A detailed case study in the Kuqa Depression of the Tarim Basin in western China shows that the simulation results and predictions agree well with field evaluations. Key words : Basin modeling; Secondary migration; Black-oil model; Finite volume method; PEBI gridding; Kuqa Depression
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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 | 0.001 |
| 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".