Effect of Variation in Fractures Conductivity and Well Location on Pressure Transient Response from Fractured Reservoirs
Bibliographic record
Abstract
Abstract Fractures often influence production behaviour in hydrocarbon reservoirs, yet the pressure transients observed in the wells may not show the conventional well-test signatures. In this case, the effect of fractures on production would be misinterperted or even completely missed. Fracture networks are commonly multi-scale and properties including aperture (or conductivity), length, connectivity and distribution vary greatly within a reservoir. The heterogeneous nature of fractured reservoirs make them very difficult to characterise and develop. In addition, the location of a producer within the fracture network also control flow rates and affect the pressure response; however, conventional well-test analysis assumes that the producer is located in symmetrical fracture networks. To improve our understanding of fracture flow behaviour from well-test data, and in order to better characterise the impact of fractures on reservoir performance, we investigate the effect of variations in fracture conductivity and location of the producer in the fracture network on the pressure transient responses. Naturally fractured reservoirs (NFR) with well-connected fracture networks are traditionally simulated using the Dual-Porosity (DP) model. However, several studies have shown that the classic DP response (V-shape) corresponding to the DP model is an exceptional behaviour applicable only to certain reservoir geology and does not apply to all NFR. To overcome the limitations of the characteristic flow behaviour inherent to this model, we employ Discrete Fracture Matrix (DFM) modelling technique and an unstructured-grid reservoir simulator to generate synthetic pressure transients in all fracture networks that we analysed. Our rigorous and systematic geoengineering workflow enables us to correlate the pressure transients to the known geological features of the simulated reservoir model. We observed that depending on the location of the producer in the fracture network and the properties of the fractures that the producer intercepts, the synthetic pressure transients vary significantly. We therefore use these insights to quantify the impact of variation in fracture conductivity and producer location on fracture flow behaviour and systematically present interpretations to these behaviours. Our findings enable us to interpret some unconventional features of intersecting fractures with variable conductivity. We observed that the behaviour of two intersecting fractures where 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 NFR with both finite- and infinite-conductivity fractures would yield a dual-porosity response that may otherwise be absent if the fracture network is assumed to have uniform conductivity.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".