Integrated Modeling of the Fractured Carbonate Midale Field and Sensitivity Analysis Through Experimental Design
Bibliographic record
Abstract
Summary We used an integrated solution by combining "direct" and "inverse" approaches to fracture network characterization in a stochastic numerical model. Static geological data obtained from cores and well logs were used together with dynamic data such as well-test responses to build 3D discrete fracture-network (DFN) models. We used the data obtained from the fractured carbonate Midale field in Canada. The fractured-reservoir model was constructed from static and dynamic (drawdown and pulse-interference tests) data. Matrix and several fracture parameters including fracture length, density/ spacing, aperture, connectivity, and orientation were evaluated in a quantitative sensitivity study to determine which characteristics have a higher influence on the accurate match to well-test response. We used experimental design to optimize the number of simulations needed for a sensitivity study and history match. The sensitivity analysis revealed a strong influence of matrix quality on the pressure response, suggesting that the history match can be specific to the simulated process and not necessarily unique. The results emphasize the contribution of matrix in the Midale reservoir and the need to simulate a broader range of processes for an accurate description of the fracture/matrix system dynamics. In a general sense, the approach used in this study proved to be useful in integrating fracture data from different sources and assessing its reliability and relative importance.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".