CHARACTERIZATION OF FRACTAL-LIKE FRACTURE NETWORK USING TRACER FLOWBACK TESTS FOR A MULTIFRACTURED HORIZONTAL WELL IN A TIGHT FORMATION
Bibliographic record
Abstract
Horizontal drilling in combination with hydraulic fracturing has been successfully used to efficiently and effectively exploit tight oil/ gas reservoirs where a multibranched fracture network may be generated near a horizontal well which has been confirmed with microseismic events (MSE). Due to their inherent constraints, limited attempts have been made to characterize such complex fracture networks at an individual fracturing stage using tracer flowback tests. In this work, numerical models have been developed, validated, and applied to describe the tracer flowback behavior and characterize the complex fractal-like discrete fracture networks in a tight formation. To be specific, the embedded discrete fracture model (EDFM) is employed to accurately capture the fracture geometry by first discretizing the fractures into various segments and then incorporating such discretized fractures into the matrix. The perpendicular bisector (PEBI) grids are then generated to flexibly conform to the fractures and reduce the grid orientation effects. Subsequently, a reservoir with complex fracture networks is discretized into two separate domains (i.e. matrix and fracture), while nonneighboring connections (NNCs) which can provide fluid communications between the two domains are applied to couple the matrix and embedded fractures. Furthermore, the tracer flowback profiles, which can reveal the complexity of fractal-like discrete fracture networks, are quantified by considering tracer advection, dispersion, and adsorption. Sensitivity analysis has been conducted to examine the effects of iteration number, branch number, deviation angle, scale factor, and the ratio of permeabilities of two cluster fractures on the tracer flowback concentration curves. It is found that the iteration number will greatly affect the tracer flowback concentration, while the deviation angle imposes a minor effect on tracer flowback behavior. An increase in both the branch number and scale factor will decrease the tracer flowback concentration. Two concentration peaks will appear when the permeabilities of the two cluster fractures are different. The larger the difference between the permeabilities of the two cluster fractures is, the greater the difference in the peak arrival time and the peak amplitude will be. In addition, the newly proposed model was verified and then applied to a field case to characterize the complex fractal-like fracture networks, which are confirmed with the microseismic events.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".