Pressure-Transient Responses of Fractures With Variable Conductivity and Asymmetric Well Location
Bibliographic record
Abstract
Summary Fractures often influence production in hydrocarbon reservoirs, yet the pressure transients observed in the wells might not show the conventional well-test signatures. In this case, the effect of fractures on production would be misinterpreted or even completely missed. The heterogeneous nature of fractured reservoirs makes them difficult to characterize and develop. In addition, the location of a producer within the fracture network also affects the pressure response; however, conventional well-test analysis assumes that the producer is located in symmetrical fracture networks. In this paper we investigate the effects of variations in fracture conductivity and location of the producer in the fracture network on the pressure-transient responses. To overcome the limitations of the dual-porosity (DP) model, this study uses a discrete fracture/matrix (DFM) modeling technique and an unstructured-grid reservoir simulator to generate pressure transients in all analyzed fracture networks. Our rigorous and systematic geoengineering work flow enables us to correlate the pressure transients to the known geological features of the simulated reservoir model. We observed that the simulated pressure transients vary significantly depending on the location of the producer in the fracture network and the properties of the fractures that the producer intercepts. Our findings enable us to interpret some unconventional features of intersecting fractures with variable conductivity. We observed that the behavior of two intersecting fractures, in which the well asymmetrically intercepts a finite-conductivity fracture, can be similar to that of a well intercepting a fracture in a connected fracture network with uniform fracture conductivity. Furthermore, a well intercepting a finite-conductivity fracture in naturally fractured reservoirs (NFRs) with both finite- and infinite-conductivity fractures would yield a DP response (V-shape) that might otherwise be absent if the fracture network is assumed to have uniform conductivity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".