Characterization of Fracture Dynamic Parameters to Simulate Naturally Fractured Reservoirs
Bibliographic record
Abstract
Abstract Fractures identification is essential during exploration, drilling and well completion of naturally fractured reservoirs since they have a significant impact on flow contribution. There are different methods to characterize these systems based on formation properties and fluid flow behaviour such as logging and testing. Pressure-transient testing has long been recognized as a reservoir characterization tool. Although welltest analysis is a recommended technique for fracture evaluation, but its use is still not well understood. Analysis of pressure transient data provides dynamic reservoir properties such as average permeability, fracture storativity and fracture conductivity. An infusion of geological knowledge helps reducing uncertainty associated with any well-test interpretation. The static properties of naturally fractured reservoirs such as fracture aperture, fracture spacing and fracture porosity can be obtained from processing of Image Log data. Simulation of naturally fractured reservoirs needs defining fracture permeability, shape factor and fracture porosity in the fracture model. However, in most simulation studies, due to high uncertainties in estimating fracture permeability and shape factor values, these parameters are initially assumed in the model and they are usually tuned during history matching which can be time consuming and also affect other history match parameters. Reservoir simulation results and predictions might be inaccurate if the values of fracture properties in the model are not reliable. This paper shows using image log data associated with welltest analysis in order to determine dynamic fracture parameters such as fracture permeability and shape factor for reservoir simulation. In this study, sensitivity analysis has also been performed on fracture permeability, fracture porosity and shape factor in a real simulation model to show importance of accurate determination of fracture parameters. Introduction Naturally fractured reservoirs differ from homogeneous reservoirs from many points of view: geological, petrophysical, production and economics. We may think of fractured reservoirs as initially homogeneous systems whose physical properties have been deformed or altered during their deposition. As a consequence, it is not always easy to match the behavior of these systems, specifically to forecast their production during simulation.
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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 | 0.001 | 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".