Analysis of Oil Production Behavior for the Fractured Basement Reservoir Using Hybrid Discrete Fractured Network Approach
Bibliographic record
Abstract
Unlike naturally fractured reservoir, fractured basement reservoir (FBR) has almost non-permeable matrix and flow is strongly dependent upon fracture network. This might cause the rapid changing behavior on oil production whether fracture near wellbore is saturated with either oil or water. In this aspect, realistic representation of fracture network is essential in FBR. Therefore the simulation of FBR is generally applied by dual-porosity (DP) continuum approach because discrete fractured network (DFN) simulator with multiphase flow is not commercially available except in-house model. In this paper, hybrid DFN approach is applied, which is continuum model coupled with local grid refinement (LGR). LGR is adapted at the cells which are passing through fractures, in order to represent fracture width less than 0.1 ft. Up to now, LGR is mostly used for well block rather than the fracture. In this approach, well control volume can not be described by LGR cell, thus, four-leg horizontal well concept substitutes the vertical well with the use of equivalent wellbore radius for overcoming the numerical convergence problem. The application of hybrid DFN approach for FBR is discussed about investigation of the possibility for drastic change on oil production. Based on the results, in fractured reservoir using hybrid DFN approach, oil production is not found to be proportional to the magnitude of matrix permeability, not as in porous system with dual-porosity approach. Also, we realized that oil production is once dropped it can not be recovered back to previous level in FBR. This is because oil-saturated fracture near well is once changed to water-saturated, then, there was not anymore changes occurred within the same fracture. Key words : Dual-porosity; Hybrid DFN; Fractured basement reservoir; Local grid refinement
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".