Dynamic Updating of Reservoir Models
Bibliographic record
Abstract
Abstract In sandstone reservoirs, one of the most important challenges is the effective upscaling of a fine scale model. A conventional upscaling process is not adequate when significant percentage of shale distribution exists in the reservoir. In the fine scale model, significant discontinuity exists in sand bodies. Some sand bodies are connected to the wells, and some are not. As the fine scale model is upscaled, some discontinuous sand bodies are combined with other connected sands. This results in two potential problems: the connected volume to the existing wells increases, thus making the production from those wells more optimistic, and the production from in fill wells do not show as much additional potential since some of the new volumes which should have been connected to the new well are already drained by the existing wells. A new procedure1 is developed to overcome this problem. In a new procedure, we first determine the connected sand volume to the existing wells. We remove the dis-connected sand bodies from the fine scale model. We then upscale the model to a desired level. We simulate the flow performance till a desired time when either new wells are drilled or some well are shut-in. At that point, we start from the fine scale model again and determine new connected volume due to additional of new wells. We combine the new virgin sands with depleted sands in the upscaled model and determine the saturation and pressure in the upscaled model using an appropriate material balance technique. We re-start the simulation using newly connected volume till we reach a point of drilling additional wells. The key difference between the proposed method and the existing methods, is our ability to add new hydrocarbon volumes (as well as new conductivity) in the model as a function of time. The proposed method was applied to a giant oil field in Siberia which is in turbite environment with large amounts of discontinuous sand bodies. We were able to demonstrate the advantage of the proposed method by comparing the performance of upscaled simulation model to the fine scale geologic model. As the percentage of sand decreases in a given reservoir, the difference between the conventional and a proposed method becomes significant. Using the new approach, we would be able to evaluate the infill potential much more accurately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".