Effectiveness of embedded discontinuities technique in capturing geomechanical behavior in naturally fractured reservoirs
Bibliographic record
Abstract
Abstract This paper highlights the efficacy of the finite element method with embedded strong discontinuities in modeling discontinuities in porous media, specifically in the geomechanical behavior of Naturally Fractured Reservoirs (NFRs). The approach considers hydromechanical coupling and offers low computational cost. NFRs account for a significant portion of global reserves, representing approximately 60% of global oil reserves and 40% of gas reserves. Given that flow in NFRs is more complex than in conventional reservoirs due to the presence of multiple fractures, it's crucial to understand how pressure variations or effective stress during operations impact fracture closure and permeability of these reservoirs. To analyze this behavior, numerical simulation results using the proposed method were compared, under different liquid pressure depletion values, with the approach proposed by Oda, which is commonly used in commercial software for calculating fracture permeability tensors. This approach was enriched with Barton's fracture closure formulation and updates on rock matrix porosity and permeability. Four simulations were conducted: Firstly, a hypothetical scenario consistent with Oda's assumptions, where fractures are interconnected and span the entire grid cell, to validate the numerical hydromechanical model; subsequently, three representative sections of a Brazilian pre-salt carbonate reservoir were selected. The study confirms the efficacy of the technique of embedded strong discontinuities in calculating equivalent permeabilities in NFRs, considering geomechanical effects, especially in cells with high fracture frequencies and intensities. Furthermore, the relevance of analyzing the geomechanical behavior in NFRs is emphasized.
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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 | 0.001 | 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".