Analysing the limitations of the dual-porosity response during well tests in naturally fractured reservoirs
Bibliographic record
Abstract
Geological reservoirs can be extensively fractured but the well-test signatures observed in the wells may not show a pressure transient response that is representative of naturally fractured reservoirs (NFRs): for example, one that indicates two distinct pore systems (i.e. the mobile fractures and immobile matrix). Yet, the production behaviour may still be influenced by these fractures. To improve the exploitation of hydrocarbons from NFRs, we therefore need to improve our understanding of fluid-flow behaviour in fractures. Multiple techniques are used to detect the presence and extent of fractures in a reservoir. Of particular interest to this work is the analysis of well-test data in order to interpret the flow behaviour in an NFR. An important concept for interpreting well-test data from an NFR is the theory of dual-porosity model. However, several studies pointed out that the dual-porosity model may not be appropriate for interpreting well tests from all fractured reservoirs. This paper therefore uses geological well-testing insights to explore the limitations of the characteristic flow behaviour inherent to the dual-porosity model in interpreting well-test data from Type II and III NFRs of Nelson's classification. To achieve this, we apply a geoengineering workflow with discrete fracture matrix (DFM) modelling techniques and unstructured-grid reservoir simulations to generate synthetic pressure transient data in both idealized fracture geometries and real fracture networks mapped in an outcrop of the Jandaira Formation. We also present key reservoir features that account for the classic V-shape pressure derivative response in NFRs. These include effects of fracture skin, a very tight matrix permeability and wells intersecting a minor, unconnected fracture close to a large fracture or fracture network. Our findings apply to both connected and disconnected fracture networks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".