Flow diagnostics for naturally fractured reservoirs
Bibliographic record
Abstract
Reliable production forecasting for fractured carbonate reservoirs is a challenge. Natural fractures, adverse wettability and complex matrix heterogeneity are all uncertain and can all negatively impact upon recovery. Ideally, we should consider different reservoir concepts encapsulated in a large ensemble of reservoir models to quantify the impact of these and other geological uncertainties on reservoir performance. However, the computational cost of considering many scenarios can be significant, especially for dual porosity/permeability models, rendering robust uncertainty quantification impractical for most asset teams. Flow diagnostics provide a complement to full-physics simulations for comparing models. Flow diagnostics approximate the dynamic response of the reservoir in seconds. In this paper we describe the extension of flow diagnostics to dual porosity models for naturally fractured reservoirs. Our new diagnostic tools link the advective time of flight in the fractures to the transfer from the matrix, identifying regions where transfer and flux are not in balance leading to poor matrix oil sweep and early breakthrough. Our new diagnostics tools have been applied to a real field case and are shown to compare well with full-physics simulation results. Thematic collection: This article is part of the Naturally Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/naturally-fractured-reservoirs
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".