Measurement and Modeling of Viscous Oil and Tar Mat Formation with a Single, Low-maturity Charge
Bibliographic record
Abstract
Tar mats are common features in carbonate and sandstone petroleum reservoirs in many basins throughout the world and greatly reduce fluid flow through affected rock. They are comprised in part by a solid or very viscous carbonaceous organic phase deposited in the rock pore spaces and in fractures and frequently found at the bottom of the reservoirs at the oil–water contact (OWC). Tar mats can be thin or quite thick (e.g., 10 m thick) and can be patchy or laterally continuous across the entire OWC of a reservoir. Tar mats and possible associated viscous oil have a huge impact on pressure support and aquifer sweep during oil production. In spite of their importance, two key properties of tar mats have not been explained in the literature: (1) the mechanism of their formation and (2) why tar mats are at the base of the reservoir. In this article, these two questions are resolved; the predominant mechanisms of formation of a tar mat and viscous oil column is clarified for a large reservoir representative of a simple class of reservoirs; those with a single low maturity charge, and with no alteration processes such as biodegradation. Here, extensive chemical analysis of the tar and viscous oil is reviewed constraining possible explanations for the origin of the tar mat. The formation of the tar mat and viscous oil is demonstrated numerically using reservoir flow simulation (Eclipse) using 2D model simulations. First-principles fluid mechanics considerations support simulation models. The nanocolloidal characterization of asphaltenes in oil codified by the Yen–Mullins model is key to predicting viscous oil and tar mat distributions at the 100 km length scale. Boycott convection is responsible for transport of asphaltene gravity currents across reservoir length scales. In addition, the role of Boycott convection in inhibiting tar mat formation clarifies why tar mats are generally form only after the reservoir charge is complete, and thus found at or near the OWC. With the understanding and modeling of this “simple” process of viscous oil and tar mat formation, more complex processes such as those involving multiple, incompatible charges are now readily accessible to reservoir simulation and for forecasting production especially with water injection.
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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) | 0.000 | 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 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".