Evaluating the Effect of Natural Fractures on Production from Hydraulically Fractured Wells Using Discrete Fracture Network Models
Bibliographic record
Abstract
Summary URTeC 1581931 Many unconventional reservoirs contain natural fractures. These fractures may be non-conductive but open preferentially during hydraulic fracturing treatment, or they may be conductive prior to treatment and provide an enlarged tributary drainage volume with different lateral extents than those suggested by conventional models of unconventional reservoirs. This paper presents a Discrete Fracture Network (DFN) study of gas production from an Eastern unconventional reservoir that contains pre-existing, conductive fractures. The natural fractures are known through a combination of innovative flow logging during drilling, image logging of the wells, and Tomographic Fracture Imaging™ (TFI). Chemical frac-tracer monitoring confirms that a natural fracture network accesses a considerably larger volume of rock than the microseismic data alone would indicate. The results of these methods provide the basis for constructing a discrete fracture network model that honors the conventional microseismic data, the flow logs, and the TFI fractures. Simulations of gas production from this network model show that, although the major portion of production comes from the hydraulic fractures and nearby closely-spaced natural fractures, the tributary drainage volume of the well extends well beyond the footprint of the hydraulic fractures themselves.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".