Improving Reservoir Characterisation and Simulation with Near Wellbore Modeling
Bibliographic record
Abstract
Abstract New reservoir characterisation methods are needed to integrate multi-scale exploration and development data, particularly at the interface between well and field models. In this paper we illustrate a novel workflow involving high resolution Near Wellbore Modeling (NWM), which allows us to accurately include seismic, wire-line data, FMI, and well core logs from multi-porosity reservoirs in field-scale reservoir simulations. We demonstrate that NWM improves reservoir characterization and production management. The workflow was applied to a realistic clastic reservoir with high variance at small scale and can also be extended for carbonate reservoirs. We have performed a number of sensitivities comparing conventional local grid refinement in the near wellbore region with that involving NWM and obtained a significant increase in the accuracy of reservoir characterization and the calibration of dynamic models. Centimetre-scale models, containing several million cells, representing the fine geological details of the near-wellbore region were constructed using available data from seismic, core, open-hole and production well-log suits. Sensitivities were performed using these high-resolution models to obtain regular grids with the best possible up-scaling. The resulting well models were imported into a field-scale simulation model to evaluate the dynamic behavior of the reservoir employing numerical well testing. Our results show that using NWM tools for reservoir modeling yields more precise flow calculations and improves our fundamental understanding of the interactions between the reservoir and the wellbore.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".