Improving Reservoir Characterization and Simulation With Near-Wellbore Modeling
Bibliographic record
Abstract
Summary New reservoir characterization methods are needed to integrate multiscale 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, wireline data, image logs, and well core logs from highly heterogeneous reservoirs in field-scale reservoir simulations. We demonstrate that an NWM-enhanced geoengineering workflow has the potential to improve reservoir characterization by applying it to a realistic clastic reservoir with high variance at small scale. We have performed a number of sensitivities comparing conventional local grid refinement (LGR) in the near-wellbore region with that involving NWM, and we obtained a significant increase in the accuracy of reservoir characterization and the calibration of dynamic models. Centimeter-scale models, containing several million cells, representing the fine geological details of the near-wellbore region, were constructed with available data from core and openhole well-log suits. The resulting well models were upscaled into regular grids with the highest resolution possible through the NWM software and incorporated into a field-scale simulation model to evaluate the dynamic behavior of the reservoir with a static-model transient test. Our results show that the use of 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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".