Field-Scale Modeling of Interwell Tracer Flow Behavior to Characterize Complex Fracture Networks Based on the Embedded Discrete Fracture Model in a Naturally Fractured Reservoir
Bibliographic record
Abstract
Summary In a naturally fractured reservoir, natural fractures can not only provide main paths for fluid flow and increase its permeability but also complicate flow behavior and production performance. Interwell tracer tests have been widely applied to estimate the petrophysical properties; however, limited attempts have been made to accurately identify the natural fracture networks. In this study, the newly proposed numerical models have been verified and used to characterize the fracture distributions in a naturally fractured reservoir conditioned to tracer transport behavior. The stochastic fracture modeling approach is implemented to generate the randomly distributed natural fractures which are dealt with the embedded discrete fracture model (EDFM) while ensuring its sufficient accuracy. To be specific, the matrix domain is discretized using the structured grids, within which each embedded fracture is divided into a series of segments. Subsequently, nonneighboring connections (NNCs) allow us to couple the flow of fluid and tracer between the nonneighboring grid cells, while the historical tracer profiles are matched to delineate the geometry and properties of the fractures by taking multiple tracer transport mechanisms into account. Furthermore, the influences of fracture number, fracture length, fracture orientation, and tracer dispersion on the tracer production concentration have been investigated through sensitivity analysis. The response of an interwell tracer model is sensitive to the fracture parameters rather than tracer properties. A fracture network with its orientation parallel to the mainstream direction will cause the earliest tracer breakthrough. The tracer breakthrough time with an average fracture length of 40 m is 110 days earlier than that with a mean fracture length value of 10 m, while the tracer production peak concentration for the former is nearly two times higher than for the latter. A larger fracture number results in an earlier tracer breakthrough, and an intermediate fracture number will lead to the highest tracer production concentration. Additionally, the newly developed model has been validated through its comparison with the commercial ECLIPSE simulator and then extended to field applications to identify the possible fracture distributions by simulating multiwell tracer tests in the Midale field. The flexible and pragmatic EDFM-based method developed in this study can model the interwell tracer flow behavior as well as characterize the properties and geometries of the natural fractures with better accuracy and calculation efficiency in comparison with other fracture simulation methods (e.g., local grid refinement method).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".