Integrated Modeling and Statistical Analysis of 3-D Fracture Network of the Midale Field
Bibliographic record
Abstract
Abstract As the maturation of conventional oil reserves pushes the industry to explore challenging reserves, state-of-the-art reservoir characterization becomes an integral part of any exploration and production venture. Naturally fractured reservoirs are good examples of such challenging fields. Oil recovery performance estimation from such reservoirs requires a good understanding of reservoir structure and its effect on the dynamics of the process. Addressed in this work is one of the critical issues for fractured reservoirs—that is characterization and 3-D modeling of a fracture network. In this study, we employed 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 response to build 3-D discrete fracture network models. We utilized the data obtained from the fractured carbonate Midale field in Canada. The on-going CO2 injection project requires a reliable description of the fracture system and matrix characteristics in the field for reliable performance analysis. Fracture network constructed from static data was calibrated and validated using well test (interference drawdown and pulse) data. Matrix and several fracture parameters including fracture length, density/spacing, aperture, connectivity, and orientation were evaluated in sensitivity studies to determine which characteristics have a higher influence on the accurate match to well test response. We utilized the factorial 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. Geological conditions and fracture properties specific to this field explained such distribution of matrix and fracture influence. Through this analysis we were able to clarify the role of fractures in the overall field performance. Matrix/fracture interaction was suggested to be a factor deserving attention. In a general sense, the approach used in this study proved to be useful to integrate fracture data from different sources, as well as to assess 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".